Sendhil Kumar, K. S., Anbarasi, M., Shanmugam, G. S., & Shankar, A. (2020). Efficient predictive model for utilization of computing resources using machine learning techniques. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 351–357. This resource proposes a technique that utilizes machine learning work to re-phrase prediction as an optimization problem.
This resource gives an overview of what clustering is and the different methods of clustering, along with its examples.
Ramona Marge, Stefan Iovan, & Alina-Anabela Iovan. (2018). Predictive analysis in the big data era. Analele Universităţii “Constantin Brâncuşi” Din Târgu Jiu: Seria Inginerie, 2018(4), 124–129. This resource highlights insights that are the basis of operational excellence, providing a significant competitive advantage and leading to business success.
Waisakurnia, W. (2020, June 12). The easiest way to interpret clustering result – Describing clusters with a function call. This resource highlights how to understand or interpret the clustering result using Python code.
Sengan, S., Sagar, R. V., Ramesh, R., Khalaf, O. I., & Dhanapal, R. (2021). The optimization of reconfigured real-time datasets for improving classification performance of machine learning algorithms. Mathematics in Engineering, Science & Aerospace (MESA), 12(1), 43–54. This resource analyzes the effectiveness of different model classification for machine learning (ML) to prevent the use of personalized log data.