To design and develop a machine learning-based personalized learning system that analyzes student academic performance, behavior patterns, and engagement metrics to recommend customized study materials and improvement strategies, thereby enhancing learning efficiency, student performance, and adaptive teaching methodologies in educational institutions.
Conduct requirement analysis to identify student performance indicators such as marks, attendance, assignment scores, and participation levels.
Collect academic dataset (real or simulated) and perform data cleaning and preprocessing.
Apply feature engineering techniques to identify meaningful attributes influencing performance.
Implement supervised machine learning algorithms such as Decision Tree, Random Forest, or Logistic Regression for prediction modeling.
Compare model performance using accuracy, precision, recall, and F1-score metrics.
Develop a recommendation engine that suggests personalized learning resources based on predicted weaknesses.
Design a user-friendly dashboard for students and teachers using web technologies (HTML, CSS, JavaScript, or React).
Integrate backend using Python (Flask/Django) or Node.js.
Visualize analytics using charts and graphs to display performance trends.
Test the system with sample users and evaluate recommendation effectiveness.
Prepare project documentation including architecture diagram, ER diagram, and system flow.
Deploy the application on a cloud platform or local server.
