A Machine Learning Ensemble Approach for Sustainable Economic Development

Abstract: According to multigenerational values for future generations, the concept of sustainability primarily refers to the preservation of economic, human, and natural resources so that they do not abruptly deplete. As part of economic sustainability, Gini index is commonly used. The Gini index measures the income inequality distribution across the nation. More precisely, Gini indexes provide an economic summary of the data, showing how fairly the resources are distributed among the population. In this research, the sustainable economic growth of the numerous amounts of nations was explored in terms of Gini index using the ensemble technique. In order to build and improve the machine learning model’s predictive accuracy, three different ensemble methods—the AdaBoost Classifier (Ada-BC), Gradient Boosting Classifier (GBC), and XGBoost Classifier (XG-BC) are utilized along with hyperparameter tuning in the dataset. By combining several weak learners into a single strong learner, boosting algorithm improves the accuracy and performance of machine learning models. To obtain the final prediction, a stacking model was also constructed using the decision tree, random forest algorithm. The results of this study may assist in predicting when the distribution of income or consumption within a country deviates from an evenly distributed distribution. In addition, it contributes to the reduction of annual consumption, spending, savings, and emissions.

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