Abstract
In a developing country like India, the growth of its citizens and consequently the advancement of the nation depend on the education provided to them. However, the process of delivering education has been hindered by considerable dropout rates which have multiple social and economic consequences. Hence, it is crucial to find out ways to overcome this problem. The advent of machine learning and the availability of an immense amount of data have enabled the development of data science and consequently, its application in educational institutions. Educational data mining enables the educator/teacher to monitor student requirement and provides the necessary response and counselling. In this paper, we use advance machine learning algorithms like logistic regression, decision trees and K-nearest neighbours to predict whether a student will drop out or continue his/her education. The accuracy of such models is calculated and studied. On the basis of the results, it was found that ML techniques prove to be useful in this domain with random forest being the most accurate classifier for predicting dropout rate. Educational institutions can analyse which students may need more attention using this research as it is base, thus modifying teaching methods to achieve the end goal of 0% dropout rate.
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Makhloga, V.S., Raheja, K., Jain, R., Bhattacharya, O. (2021). Machine Learning Algorithms to Predict Potential Dropout in High School. In: Khanna, A., Gupta, D., Pólkowski, Z., Bhattacharyya, S., Castillo, O. (eds) Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-8335-3_17
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