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As we probably are aware, grouping assumes a significant part in each field. Support vector machine is the famous calculation for characterization and expectation. For grouping and expectation by help vector machine, LIBSVM is being utilized as a device. Support vector machine orders the information focuses utilizing straight line. Some datasets are difficult to isolate by straight line. To adapt to this issue portion capability is utilized. The focal thought of bit capability is to extend face up in a higher layered space trusting that distinctness of information would get to the next level. There are different parts in the LIBSVM bundle. In this paper, Backing Vector Machine (SVM) is assessed as classifier with four unique parts to be specific straight portion, polynomial piece, spiral premise capability bit and sigmoid bit. A few datasets are being tested to figure out the exhibition of different portions of help vector machines. In view of the best exhibition result, straight piece is fit for grouping datasets precisely with the typical exactness 88.20 % of right order and quicker with 4.078 sec of expectation time. Outspread premise capability Bit is fit for taking less preparation time contrasted with different parts that is 4.92675 sec. Key Word: SVM, Libsvm, Pieces, order