Abstract
The employability of students has been a major concern for all academic institutions. Academic organizations are keen to analyze multi-faceted performance-centric data of the students to enhance their performance outcome. The concept of data mining on educational data which is also known as Educational Data Mining (EDM) has gained much interest to facilitate this need for performance analysis. It helps to extract meaningful information from raw academic data. This work uses a dataset developed with academic performances along with test scores and applies those data for classification using a decision tree classifier to predict the employability of students across different disciplines. The experimental study has shown that the decision tree classifier for employability prediction yields high accuracy.
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Patro, C., Pan, I. (2021). Decision Tree-Based Classification Model to Predict Student Employability. In: Pan, I., Mukherjee, A., Piuri, V. (eds) Proceedings of Research and Applications in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1543-6_32
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DOI: https://doi.org/10.1007/978-981-16-1543-6_32
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