Before approving loans or buying securities, investors analyze the sovereign credit rating of a country that shows its ability to fulfill obligations. This information plays an important role for both, the debtor and the creditor. Calculation of this rating is performed by specialized agencies that provide their opinions based on appropriate information. It is expressed in the form of different categories and their calculation models are not publicly available. A country's credit rating shows how likely it is that the country will fulfill its obligations as a debtor on time. There are a lot of different opinions about the indicators that determine credit ratings and methods of their calculation. As data mining finds application in the economic sphere, the question is how successful are algorithms in determining country’s credit rating. The aim of this paper is to use the data mining classification technique on selected data sets in order to predict sovereign credit rating. The methods used in this paper are Naive Bayes, k-nearest neighbours, decision tree and random forest. Evaluation measures of the models are presented and interpreted.

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