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Analysis of value added services on GDP Growth Rate using Data Mining Techniques

Stefan Preda ()
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Stefan Preda: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2018, vol. 9, issue 1, 29-38

Abstract: The growth of Information Technology has spawned large amount of databases and huge data in numerous areas. The research in databases and information technology has given rise to an approach to store and manipulate this data for further decision making. In this paper certain data mining techniques were adopted to analyze the data that shows relevance with desired attributes. Regression technique was adopted to help us find out the influence of Agriculture, Service and Manufacturing on the performance of gross domestic product (GDP). Trend and time series technique was applied to the data to help us find out what trend of GDP with respect to service, agriculture and manufacturing sector for the past decade has been. Finally Correlation was also used to help us analyze the relationship among the variables (service, agriculture and manufacturing sector). From the three techniques analyzed, service value added variable was the most prominent variable which showed the strong influence on GDP growth rate.

Keywords: Machine Learning(ML); Support Vector Machine(SVM) (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:9:y:2018:i:1:p:30-39

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