Student dropout is a major challenge in the education system, affecting students' academic progress and future career opportunities. This project, Student Dropout Prediction Using Machine Learning Techniques, aims to identify students who may be at risk of dropping out by analyzing student-related data. The system uses supervised machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest to analyze the dataset and predict student outcomes. The dataset is preprocessed to handle missing values and prepare the data for model training. The performance of the algorithms is evaluated using metrics such as accuracy, precision, recall, and F1-score to identify the most suitable model. The proposed system can help educational institutions recognize students who may need additional academic support and take preventive measures at an early stage. This project demonstrates the potential of machine learning in educational data analysis and supports data-driven decision-making to improve student retention and academic success.

Mahalakshmi.A, Bhavana.S, Karthick Prasath.B, Chaithra.D,"Student Dropout Prediction Using Machine Learning" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(1020-1025) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.