Rough set 4Rough sets used for classification to discover structural relationships within inexact or noisy data. They are a mathematical concept dealing with uncertainty in data, developed by Pawlak in 1982. It based on the creation of equivalence classes within the given training data. All data in equivalence classes are indiscernible. A rough set definition for a class is approximated by two sets: a lower approximation of the class and an upper approximation of the class. The lower approximation of the class consists of all the data tuples that, based on the knowledge of the attributes, are certain to belong to the class without ambiguity. The upper approximation of the class consists of all the tuples that, based on the knowledge of the attributes, cannot be described as not belonging to the class. Rough sets can also be used for attributes selection (where attributes that do not contribute to the classification of the given training data can be identified and removed) and relevance analysis (where the contribution or significance of each attribute is assessed with respect to the classification task).FG.One main advantage is that rough sets do not need any pre-assumptions or preliminary information about the data AThe Rough Set theory is applied to pre-process the information and obtain core attribute as internal nodes.Support Vector Machine (SVM)SVMs are a set of related supervised learning methods used for classification and regression 10. Is an algorithm that attempts to find a linear separator (hyper-plane) between the data points of two classes in multidimensional space. SVMs are well suited to dealing with interactions among features and redundant features12.SVMs are supervised learning models with associated learning algorithms that analyze data and recognize patterns. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier11.