To build an AI-powered predictive analytics system that identifies students at risk of low engagement or dropout by analyzing attendance, academic performance, and behavioral patterns, enabling early intervention strategies to improve retention rates and institutional academic success.
Define key engagement and dropout indicators such as attendance frequency, grades, LMS activity logs, and assignment submissions.
Collect and preprocess structured educational datasets.
Perform exploratory data analysis (EDA) to identify trends and correlations.
Implement classification algorithms such as Support Vector Machine, K-Nearest Neighbors, or Gradient Boosting.
Train and validate models using cross-validation techniques.
Evaluate model performance using confusion matrix and ROC curve analysis.
Develop a risk scoring mechanism categorizing students into low, medium, and high-risk groups.
Create an admin dashboard for faculty to monitor student engagement levels.
Integrate notification alerts for high-risk students.
Use data visualization libraries such as Matplotlib or Chart.js for performance insights.
Ensure data privacy and ethical considerations in handling student data.
Document algorithm selection rationale and system architecture.
Deploy and demonstrate real-time prediction using test cases.
