مجلة النمو الاقتصادي والمقاولاتية
Volume 6, Numéro 2, Pages 16-25
2023-05-27
An Empirical Xgboost Algorithm To Predicting Which Are The Factors That Most Contribute To Boosting Women’s Entrepreneurship In The World?
Authors : Frahi Fadila .
Abstract
The purpose of this paper is to identify the most contributing factors to boosting women’s entrepreneurship by exploiting World Bank datasets related to the project: Women, Business and the Law, from 1971 to 2021 in 190 countries, using Extreme Gradient Boosting a model of Machine learning, which is a boosting algorithm based on gradient boosted decision trees algorithm. Across more than five decades of data from this project, the paper highlights the importance of empowering women, and found that specific factors are among the strongest motivators for women to take up entrepreneurship as a career choice, And insistence on preserving the extracted factors that boost their progress and contribute with strong participation in the success of institutions and company, due the impact of the latter on sustainable development in the world.
Keywords
Women’s entrepreneurship ; Machine Learning ; Extreme Gradient Boosting
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