• MS. KRISHAL RODRIGUES
• Dr. Shashidhar Kini K

This study focuses on the development of a Student Performance Prediction system using machine learning regression and classification models. The goal is to forecast academic outcomes based on key factors influencing student performance. These factors include demographic attributes, academic history, parental education, socio-economic background, and behavioral indicators. For this purpose, structured data was obtained from public educational datasets and institutional records. The analytical workflow encompasses data acquisition, preprocessing, feature engineering, and the training and evaluation of predictive models such as Logistic Regression and Random Forest. This approach aims to support early intervention strategies in educational settings.

Student Performance Prediction, Machine Learning, Educational Data Mining, Logistic Regression, Random Forest, Data Preprocessing, Academic Analytics, Predictive Modeling

"STUDENT PERFORMANCE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1092-1095, July-2025, Available :http://www.jetir.org/papers/JETIRGX06207.pdf

"STUDENT PERFORMANCE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1092-1095, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06207.pdf