AI-Powered Student Risk Prediction Model

Develop a machine learning predictive model using TensorFlow.js to identify students at risk of academic failure or dropout. Create a comprehensive system that analyzes multiple data points including attendance, assignment completion, quiz scores, and engagement metrics. Implement a neural network that can generate risk scores with over 80% accuracy, with a real-time dashboard for educators to track and intervene with at-risk students. Include feature importance analysis and model interpretability components.