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Research Paper | Statistics | Volume 15 Issue 3, March 2026 | Pages: 1521 - 1525 | India
Predicting Semester Performance of College Students Using Data Mining Techniques and WEKA Analysis
Abstract: Over the years, several statistical tools have been used to analyse and predict students? performance from different point of view. One of the biggest challenges for higher education. Today is to predict the paths of students through the educational process. Successful students? result prediction in early course stage depends on many factors. Data mining techniques could be used for this kind of job. Data mining techniques are widely used in educational field to find new hidden patterns from student?s data. The hidden patterns that are discovered can be used to understand the problem arise in the educational field. Data Mining (DM), or Knowledge Discovery in Databases (KDD), is an approach to discover useful information from large amount of data. Data mining techniques apply various methods in order to discover and extract patterns from stored data Based on collected students? information; different data mining techniques need to be used. For the purpose of this project WEKA data mining software is used for the prediction of semester wise student?s marks based on parameters in the given dataset. The dataset contains information about different students from one college of 5 courses in the overall semesters.
Keywords: Data mining techniques, Clustering methods: Canopy, EM, Hierarchical, Simple-k means
How to Cite?: M. Naresh, K. Murali, B. Mamatha, S. Upendra, K. Sreenivasulu, C. Dwarakanath Reddy, B. Sarojamma, "Predicting Semester Performance of College Students Using Data Mining Techniques and WEKA Analysis", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 1521-1525, https://www.ijsr.net/getabstract.php?paperid=SR26318145404, DOI: https://dx.dx.doi.org/10.21275/SR26318145404