Predictive Analytics in SQL Server: Leveraging Machine Learning for Web Applications

Abstract

Predictive analytics has become a critical component of modern web applications, allowing businesses to forecast trends, identify patterns, and optimize decision-making. SQL Server, with its built-in Machine Learning Services, provides an efficient platform for integrating predictive analytics directly into databases. By leveraging machine learning models, organizations can perform real-time analysis on vast datasets, enabling applications to make intelligent predictions without external dependencies. This paper explores the implementation of predictive analytics in SQL Server, detailing how machine learning algorithms such as regression, decision trees, and neural networks can enhance web applications. It also examines the performance implications, scalability challenges, and best practices for integrating predictive models within SQL Server. The study highlights the benefits of embedding machine learning into SQL-based environments, allowing for faster insights and automated decision-making.