Predictive Analytics in Business Intelligence Systems via Gaussian Processes for Regression

In today's tech landscape, business intelligence (BI) is essential, especially with advanced technologies emerging. This study delves into the transformative potential of artificial intelligence and machine learning in predictive analytics, offering crucial insights into data. The chapter provides an extensive overview, focusing on the future of predictive analytics in BI. It highlights real-time applications, featuring practical work and case studies. Key practices for integrating AI and predictive analytics are introduced, covering data preparation, cleaning, preprocessing, feature engineering, and exploratory data analysis. Integration of ML with BI facilitates visualization, report generation, interactive dashboards, and real-time monitoring. However, manual analysis of complex datasets poses challenges, risking valuable insights. This chapter explores how ML automates the identification of patterns, extracting insights. Key challenges and advantages in integrating machine learning into BI frameworks are also discussed.

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Predictive Analytics in Business Intelligence Systems via Gaussian Processes for Regression

Cited by 2 publications

References 10 publications

Impact response prediction and optimization of SC walls using machine learning algorithms

Impact response prediction and optimization of SC walls using machine learning algorithms

Leveraging AI and Machine Learning for Predictive Analytics in Business Intelligence

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