Design and develop an interactive data visualization system to analyze student academic performance across departments, semesters, and subjects. The system should help administrators and faculty identify performance trends, at-risk students, and subject-wise analytics through dynamic dashboards and predictive visual insights.
Collect academic datasets including marks, attendance, demographics, and course details.
Clean and preprocess data using Python (Pandas) or R.
Design a relational database schema to store structured academic data.
Implement backend APIs for data retrieval and aggregation.
Develop interactive dashboards using Power BI/Tableau/Plotly/D3.js.
Create visualizations such as heatmaps, bar charts, trend lines, GPA distributions, and attendance-performance correlation graphs.
Implement filtering options by department, semester, gender, and subject.
Add predictive visualization for identifying at-risk students using regression or classification models.
Integrate drill-down functionality from university level to individual student level.
Ensure responsive web-based interface for multiple devices.
Conduct usability testing with faculty members.
Document system architecture, visualization techniques, and user guide.
Present analytical findings and insights derived from the visualization system.
