Data Mining for Analyzing GEM's Entrepreneurial Growth Indicators

Abstract

Entrepreneurship has a significant influence on the economic development of a country. For this reason, entrepreneurial growth must constantly be monitored and main-tained by the government and various institutions. One of them is Global Entrepreneurship Monitoring (GEM). Every year GEM conducts two types of surveys on its member countries to measure the entrepreneurial growth of these countries. One of the surveys conducted was APS. This survey used 15 indicators. This paper discusses the application of data mining, especially regression techniques, to find the correlation among the indicators. The dataset used is the results of the APS of Asian countries from 2014 - 2021.