Abstract
Categorizing data in order to use them at their highest level of effectiveness and efficiency is called data classification and it is used to perform complex and varied actions in many different fields including the financial field. Accurate classifications can lead to accurate predictions so, the applied classification method is very important. This paper describes and compares the performances of some data classification methods applied for a real dataset. A list of pros and cons is made for each of the used method. The obtained results show that an important role for the method's level of accuracy is played by the choice of features for the considered data to classify.
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