System Implementation for Identification and Minimization of Student's Academic Failure Using Machine Learning Techniques

In educational institutions, identifying students at risk of academic failure or dropout is crucial for timely intervention and support. The Student Risk Analysis Project aims to develop a data driven system that assesses and predicts student risks based on various academic, behavioral, and socio-economic factors. By leveraging machine learning algorithms and statistical models, the system analyzes historical student data, attendance records, exam performance, participation levels, and other relevant indicators to classify students into different risk categories. The project integrates multiple data sources, including student management systems and learning analytics platforms, to provide real-time insights for educators and administrators.