Özet:

Income inequality refers to the situation where income distribution is not shared regularly and fairly. Income inequality is among the essential problems of countries in both economic and social terms. The Gini coefficient is widely used to measure income inequality. In this study, random forest, support vector algorithms, and multiple linear regression model, which are among the machine learning algorithms, were applied to estimate the Gini coefficient of Organization for Economic Co-operation and Development (OECD) countries for 2015-2018. When the models were compared according to performance criteria, the best model was the random forest model with the highest R2 = 0.7085 and the smallest RMSE = 0.0264. According to the random forest model results, the tax revenue variable has the greatest impact on the Gini coefficient. The country with the highest Gini coefficient is Mexico, and the lowest is the Slovak Republic. Also, it has been observed that the lowest tax income value belongs to Mexico.

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