Performance Prediction Model for National Level Examinations - Shanmugavadivu, P; Haritha, P; Kumar, Ashish (International Journal of Computer Sciences and Engineering, 2025)
In the recent years, usage of data mining techniques to statistically analyze the performance of candidates in academics or national level examinations is in increase. The development of predictive analytics tools and their applications are also in the rise. This paper reports on the mechanism of the proposed prediction model that predicts the performance of a candidate appearing for national level examinations. The proposed Performance Prediction Model (PPM) is designed as a framework comprising of data classification and ranking of dataset, computation of correlation coefficient that measures the dependency among the variables and prediction using linear regression. The performance of PPM is validated on UGC-NET (2016) dataset. Based on the observed correlation between Paper-II and Paper-III marks, PPM predicts the score of a candidate in Paper-III with reference to the scored marks in Paper-II. The accuracy of the predicted data is recorded as 88 per cent. The illustrative visualizations presented in this article depict the performance analysisof the candidates in Paper-I, Paper-II and Paper-III.