Placement Forecast of a Three Year Graduate Course Students using K-Nearest Neighbor (KNN) Model

  • Gaurav Sharma*

Abstract

The placement is an important part of academics. While placement of individual depends upon various criteria but some of them are attainable by students. So it will be beneficial for college authorities to check placement ability of individual students. These findings may help in timely actions by authorities to ensure that no stone remain unturned. The data of past year can be used for study of this kind of the problem. In this article KNN and various other supervised machine learning based models have been used for the study of this problem. Although the train data accuracy of 84 percent was obtained while implementing KNN-based supervised machine learning algorithm, a tenfold cross validation was conducted to further verify the model's accuracy, which guarantees the model's 82 percent accuracy.        

Published
2020-06-01
How to Cite
Gaurav Sharma*. (2020). Placement Forecast of a Three Year Graduate Course Students using K-Nearest Neighbor (KNN) Model. International Journal of Advanced Science and Technology, 29(06), 7894-7900. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/25159