Predictive Modeling of Student Academic Outcomes Using Machine Learning
1M.Swathi Reddy
Assistant Professor, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.
Email: swathi.madireddy@gmail.com
3D.SaiSakshitha
UG Student, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.
Email: saisakshitha14@gmail.com
2T. Sanjana
UG Student, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.
Email: sanjanathota2003@gmail.com
4V.Harisri
UG Student, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.
Email: harisri692005@gmail.com
Abstract -Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Predicting student academic performance is a critical task for educational institutions aiming to improve learning outcomes and provide timely support to at-risk students. With the rapid advancement of machine learning techniques, data-driven approaches have become increasingly effective in analyzing and forecasting student success. This study explores the application of various machine learning algorithms—such as Decision Trees, Random Forest, Support Vector Machines (SVM), and Logistic Regression—for predicting student performance based on historical academic records, demographic data, and behavioral factors. A dataset comprising student information including attendance, participation, past grades, socio-economic status, and other relevant features is used to train and evaluate the models. Data preprocessing techniques such as normalization, missing value imputation, and feature selection are employed to enhance model accuracy. The performance of the models is assessed using standard metrics like accuracy, precision, recall and F1 score.
Keywords-Decision tree, random forest, SVM, predictive analysis, academic records, F1-score.
