Data mining provides educational institutions that the capability to explore, visualize and analyze large amounts of data in order to reveal valuable patterns in students’ learning behaviors. Turning raw data into useful information and knowledge also enables educational institutions to improve teaching and learning practices, and to facilitate the decision-making process in educational settings. Thus, educational data mining is becoming an increasingly important with a specific focus to exploit the abundant data generated by various educational systems for enhancing teaching, learning and decision making. In EDM, Feature Selection is to choose a subset of input variables by eliminating irrelevant features. Feature Selection Algorithm has proven to be effective in enhancing learning efficiency, increasing predictive accuracy and reducing complexity of learned results. The primary objective of this research work is to investigate the most relevant subset features for achieving high performance accuracy by adopting Correlation based feature Subset Attribute evaluation and Gain-Ratio Attribute evaluation feature selection techniques. For classification, the Naïve Bayes classifier is implemented by using WEKA tool. The outcome shows the effectiveness in the predictive accuracy with minimum number of attributes. Also the results reveals that the selected data features have found to be influenced the classification process of the student performance model.