Educational visualization application based on machine learning algorithm to predict student learning

Project Description & Abstract

An intelligent educational analytics application designed to analyze student-related academic data and predict learning outcomes using machine learning algorithms and data visualization techniques. The system helps educators understand student performance, identify learning patterns, and recognize students who may require additional academic support. The application collects and analyzes factors such as attendance, previous academic performance, study behavior, assessment scores, participation, and other relevant educational attributes. Machine learning models process these data points to identify patterns and generate predictions related to student learning performance. An interactive visualization module presents the analyzed information through charts, graphs, performance indicators, and comparative reports, making complex educational data easier for teachers, administrators, and students to understand. The system can help identify students with different performance levels and provide data-driven insights that support personalized learning strategies. Educators can use these insights to monitor student progress, evaluate academic trends, and take timely intervention measures. Tags: Student Learning Prediction, Educational Data Mining, Machine Learning, Education Technology, Student Performance Prediction, Learning Analytics, Data Visualization, Academic Performance, Student Analytics, Predictive Analytics, Artificial Intelligence, Python, Flask, MySQL, Scikit-learn, Pandas