Bibliographic Details
| Title: |
Can the WEKA Data Mining Tool be Used in Developing an Economic Growth Model? |
| Authors: |
Zamir, Zahid B.1 zzamir@desu.edu |
| Superior Title: |
Journal of Accounting, Business & Management. Oct2023, Vol. 30 Issue 2, p27-36. 10p. |
| Subject Terms: |
*DATA mining, *ECONOMIC models, *ECONOMIC expansion, *TERMS of trade, *ECONOMIC forecasting, MACHINE learning |
| Company/Entity: |
WORLD Bank |
| Abstract: |
WEKA is a free and open-source software offering visual outputs and a diverse range of machine-learning algorithms. Its rich collection of algorithms sets it apart from commercial data mining systems. Inflation and economic growth, encompassing entire economies, have far-reaching impacts on individuals, either directly or indirectly. While conventional economic theories often assert a negative relationship between inflation and economic growth, researchers over the last seven decades have encountered various models and datasets both supporting and contradicting these traditional notions. In order to forecast economic growth or inflation, seven key attributes from the World Bank dataset were utilized. These attributes included variables such as war, terms of trade log change, consolidated public sector surplus, and more. The objective was to develop a decision support model using the WEKA data mining tool. Three algorithms within WEKA were employed: linear regression, ZeroR, and RepTree. Results indicate that the linear regression algorithm consistently outperformed the others. It exhibited superior predictive abilities for expected growth, confirmed through both 10-fold cross-validation and a 75% split test. WEKA's versatility, coupled with its machine learning algorithms, especially the linear regression model, provides a potent resource for exploring and predicting the intricate relationship between inflation and economic growth. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Accounting, Business & Management is the property of Journal of Accounting, Business & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Business Source Premier |