Data mining is a broad area that integrates research efforts from several fields with the aim of processing large volumes of data into knowledge bases for better decision making. Since numerical and nominal data are equally important in practical data mining applications, dealing with different types of data items are among the most important problems in data mining research and development. This paper introduces a new fuzzy rule induction algorithm, able to deal properly with either numerical or nominal attributes, for the creation of classification and predictive models. To better handle numerical data, fuzzy sets are used to represent intervals in the domains of numerical attributes. Experimental results have shown that the proposed algorithm produces robust and general models that can be used for prediction as well as for classification.