The Academic Risk Prediction System is a web-based application developed using Python and Flask that predicts the academic risk level of students.
It helps in early identification of students who may face academic difficulties by analyzing multiple parameters such as attendance, marks, study hours, backlogs, and stress levels.
Student data input form Machine Learning based prediction (Random Forest) Risk score and category (LOW, MODERATE, HIGH, CRITICAL) Explanation of risk factors Action plan for improvement PDF report generation User-friendly web interface
Python Flask Machine Learning (Random Forest - Scikit-learn) Pandas, NumPy HTML, CSS ReportLab (PDF generation)
- User enters student details (attendance, marks, etc.)
- Data is processed by the backend
- Machine learning model predicts risk
- System generates: Risk score Risk category Explanation Action plan
- PDF report is generated
git clone https://github.com/khushbu1811/Academic-Risk-Prediction-System.git
cd Academic-Risk-Prediction-System
pip install -r requirements.txt
python app.py
Integration of Machine Learning with Web Applications Flask backend development Real-world problem solving using data PDF report generation Risk-based decision support system
This project was developed as part of MCA Semester-I.
It focuses on early identification of academically at-risk students and supports faculty in decision-making through data-driven insights.
Works on single student input No database integration yet Accuracy depends on input data
Batch-wise analysis Student dashboard Faculty analytics panel ERP integration
https://github.com/khushbu1811/Academic-Risk-Prediction-System.git
Full project report available here: View Report