LEVERAGING MACHINE LEARNING FOR RESOURCE OPTIMIZATION IN USA DATA CENTERS: A FOCUS ON INCOMPLETE DATA AND BUSINESS DEVELOPMENT

Data centers form the cornerstone of modern digital infrastructure, enabling operations from e-commerce and streaming to cloud computing and artificial intelligence. The United States, at the forefront of technology, houses some of the world's most extensive and technologically advanced data centers as a part of its economic and technological framework. This study aimed to explore how different ML techniques can be used for optimizing resource utilization by data centers in the US, focusing on strategies to handle incomplete data and implications for business development. The dataset was retrieved from the GitHub repository, which provided a rich dataset of resource usage metrics and operational data from U.S. data centers. It contained complex and fine-grained information that was necessary to optimize data center performance and deal with incomplete data challenges. A detailed description of the dataset and its key attributes was provided. It was designed for analyzing resource usage patterns in data centers, putting much emphasis on energy efficiency, workload distribution, and operational reliability. This integration of time-series data with sensor readings and performance logs provided a comprehensive overview of resource consumption and environmental conditions in data center operations. This dataset was curated for the engagement of machine learning models in the study and optimization of resource consumption along with the challenges of missing data. Analysis of resource utilization in US data centers was accomplished using the application of various models for machine learning, most notably, Logistic Regression, Random Forest, and Support Vector Machines; Retrospectively, the Random Forest and SVM models seem to be robust and reliable, placing the Random Forest slightly above, given their performance is nearly perfect for training and testing. The application of machine learning techniques holds huge potential for the reformation of resource management in US data centers. These models analyze a pattern in historical data to predict future resource demands, thus allowing optimized resource allotment and minimizing operational costs.