ANALYSIS OF STUDENT’S ACADEMIC PERFORMANCE USING CLASSIFICATION ALGORITHM IN WEKA

Twinkle Chawla, Gurpreet Singh

Abstract


As we have extensive measure of information in industry so it is important to investigate the data and extract the useful information by applying distinctive data mining techniques. Data mining is used in many fields, mining related to education is called EDM. All the institutions aimed to provide good quality education to its student. Extraction of knowledge with the help of data mining techniques helps students to know their weakness and to improve it. For better results analyse the academic performance of students and the performance will depend upon various factors like annual income of family, qualification of mother, marks of 10th and 12th and so on. In this study we use techniques like Random Tree, J48, Random Forest, REP Tree in WEKA. These techniques are used to build the model and to generate results in WEKA. These classification algorithms are compared based on students’ social conditions, previous academic records using WEKA. The records of 175 computer engineering students are used to build the model. Random Forest with highest average accuracy 71.4% among other.

Keywords


Data mining, Educational Data mining, WEKA, J48, and REP Tree

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DOI: https://doi.org/10.26483/ijarcs.v8i7.4576

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