Demystifying Prescriptive Analytics Frameworks and Techniques
Selva Lakshmanan1, Madasamy Sornam2, Jimmie Flores3
1Selva Lakshmanan*, Research Student, School of Business and Technology, Aspen University, United States.
2Dr. Madasamy Sornam, Ph.D., Professor, Department of Computer Science, University of Madras, India.
3Dr. Jimmie Flores Ph.D., D.M., Dean, School of Business and Technology, Aspen University, United States.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 02, 2020. | Manuscript published on April 10, 2020. | PP: 1422-1427 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4546049620/2020©BEIESP | DOI: 10.35940/ijitee.F4546.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Big data analytics refers often a very complex process to examine the large and varied data sets to provide the organization to take smarter decisions and better results. Big data analytics is a form of advanced analytics including predictive, prescriptive models and statistical algorithms. The prescriptive analytics is a later stage of the Big data analytics which is not just anticipating what the event will happen as in the predictive analytics but also suggests the decision options and consequences of the decision. The paper addresses the survey of prescriptive analytics and the importance of prescriptive analytics. The prescriptive analytics techniques and methods include machine learning, operation research/management science, optimization techniques, mathematical formulation, and simulation techniques and methods. The paper discusses the techniques and methods, frameworks, and domain applications of prescriptive analytics.
Keywords: Machine Learning, Meta Heuristics, Optimization, Predictive Analytics, Prescriptive Analytics
Scope of the Article: Machine Learning Internet of Things