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Feature selection is the process of eliminating irrelevant features from the dataset, while maintaining acceptable classification accuracy. The selected features play an important role which can directly influence the effectiveness of the resulting classification. In this paper, a methodology is proposed consisting of two phases, attributes selection and classification based on the attributes selected. Phase one uses a filter and wrapper method for attribute selection with random over-sampling (Ros) through which the size of attributes set and misclassification error can be reduced. In the second phase, the selected attributes are taken as inputs by classification techniques like decision trees (DT), K-nearest neighbour (KNN), support vector machine (SVM), naive Bayes (NB) and artificial neural network (ANN). Finally, true churn, false churn, specificity and accuracy are measured to evaluate the efficiency of the proposed system and it is found that the above mentioned methodology performs well ahead for churn prediction and suits well for the telecommunication sector.
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