Predictive Analytics for Resource Optimization in Data Warehousing and Data Mining Using Random Forest

Abstract

The present study investigates the utilization of predictive analytics methods, particularly random forest, for the purpose of optimizing resource allocation in data warehousing and data mining settings. Organizations are depending more and more on data warehouses and data mining in the big data era to glean valuable insights from enormous datasets. Optimizing memory allocation, processing power, and storage, on the other hand, is essential to ensuring the efficacy and efficiency of these analytical procedures. In order to enable proactive resource allocation and optimization techniques, this study explores the ability of random forest models to forecast resource use trends based on historical data. As part of the research technique, historical data on resource utilization metrics in data warehousing and data mining contexts are gathered and analyzed. These measurements consist of timestamps and the matching amount of data warehouse storage used. The random forest model showed that it could identify trends in past data, which made it possible to predict future storage needs. The projections of the model function as a decision support system, giving stakeholders practical information for maximizing resource use.