• Dr. P. RAMAKRISHNA REDDY
• Dr. Hemanth Kumar Molapata
• Dr. K. Ramakrishna
• Dr. T. Mahesh Kumar

In the real life of higher education, predicting students' academic performance remains a significant challenge. Employing statistical tools and data mining techniques has become essential to analyse and forecast students' paths through the educational process. This paper explores the utilization of data mining techniques, specifically focusing on the WEKA data mining software, to predict semester-wise student marks based on parameters within a given dataset. The dataset encompasses information from students across five courses within a single college over multiple semesters. By using data mining methodologies, educators can uncover hidden patterns within student data, facilitating a deeper understanding of the factors influencing academic success. This paper discusses the application of data mining techniques as a means to discover valuable insights and improve decision-making processes within the educational domain.

Data mining techniques, WEKA tool, Clustering methods: Canopy, EM, Hierarchical, Simple-k means.

"UTILIZING DATA MINING TECHNIQUES FOR PREDICTING STUDENTS' ACADEMIC PERFORMANCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k600-k617, March-2024, Available :http://www.jetir.org/papers/JETIR2403A77.pdf

"UTILIZING DATA MINING TECHNIQUES FOR PREDICTING STUDENTS' ACADEMIC PERFORMANCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk600-k617, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A77.pdf