Descriptive and Predictive Analytics on Adventure Works Cycle: A Corporate Decision Making
Yew Liong Lim1, Raheem Mafas2

1Yew Liong Lim, School of Computing, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia.
2Raheem Mafas*, School of Computing, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 2041-2048 | Volume-8 Issue-6, March 2020. | Retrieval Number: E7007018520/2020©BEIESP | DOI: 10.35940/ijrte.E7007.038620

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Abstract: Business Analysis has become one of the crucial elements of any business in this data-driven business world. This is at the frontline where the data analytics support the strategic management to make effective decisions with immense computing power. This paper investigates the big data problems of Adventure Works Cycles (AWC) by using analytical techniques and integrate different methods of knowledge discovery and data mining via descriptive and predicative analytics. The descriptive analytics revealed the prevailing business condition which could aid to make effective decisions. Consequently, an empirical study was performed to explore different types of predictive models to predict the future occurrences. Furthermore, a comparative analysis using different predictive algorithms which provides evidence that High-Performance Forest algorithm is particularly operative on the prediction of future occurrences with the accuracy of 80%, ROC index 0.878 and the cumulative lift value of 1.82. This study provides an intuitive grasp of the concept to forecast, find patterns and rules to increase AWC’s overall sales performance and improve overall lead scoring more accurately.
Keywords: Business Analys.
Scope of the Article: Big Data Analytics and Business Intelligence.