Every Student Deserves a Path to Success
Identify at-risk students early with explainable AI. Our Random Forest model analyzes 8 key factors to predict outcomes and recommend interventions.
Model Accuracy
94.2%
Predictions
< 100ms
Data Privacy
100%
Students Analyzed
0
Ready for upload
At-Risk Detected
--
Requires intervention
Model Confidence
--%
Average prediction score
Interventions
0
Recommended actions
Upload Your Student Data
Drag and drop your CSV file, or click anywhere in this area to browse. We'll automatically detect columns and validate data quality.
Data Quality Report
Pre-training validation results
Model Training
Random Forest Classifier with 100 estimators
Trees Built
0/100
OOB Score
--
Best Split
--
Time Elapsed
0s
Accuracy
--
Precision
--
Recall
--
F1 Score
--
Confusion Matrix
Feature Importance (SHAP)
Individual Risk Assessment
Enter student metrics for instant prediction with explanation
Batch Risk Assessment
Upload multiple students and receive prioritized intervention lists
Drop prediction CSV here
Or click to browse • Results in seconds
0 Urgent Interventions Needed
All at-risk students (failure probability ≥50%)
| Student | Risk Score | Prediction | Key Factors | Recommended Action |
|---|
No students match the selected filter
What-If Analysis & Intervention Planner
Simulate the impact of different interventions on student outcomes. Use this tool to prioritize resources and maximize your intervention budget effectiveness.
Current At-Risk Rate
--%
How This Works
Our model shows that Study Hours and Attendance have the highest impact on student success. Use the sliders below to simulate increasing these factors and see projected outcomes. The simulator assumes interventions are applied to your current at-risk student population.
Intervention Parameters
Additional hours per week
Impact: High (15% model weight)
Percentage point increase
Impact: Very High (25% model weight)
Program intensity level
Impact: Medium (10% model weight via motivation)
Target optimal sleep (7-8 hrs)
Impact: Low (5% model weight) but improves overall wellness
Quick Presets
Projected Impact Analysis
Current State
--
At-Risk Students
After Intervention
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At-Risk Students
Students Saved
--
Success Rate
--%
Intervention Cost
$--
Cost Per Student
$--
Return on Investment
Based on research, each student retained saves approximately $15,000 in lost tuition and societal costs. Your projected intervention could yield $-- in value.
Intervention Breakdown
Recommended Action Plan
Understanding Student Success Factors
Attendance is Critical
Our model shows attendance is the strongest predictor of success (25% importance). Students with >90% attendance have 85% pass rate. Even small improvements here yield significant results.
Study Hours Matter
Quality study time (15% importance) beats quantity. Students studying 15-25 hours weekly perform optimally. Beyond 30 hours shows diminishing returns due to burnout.
Motivation is Multi-Factorial
Motivation (10% importance) is influenced by tutoring support, sleep quality, and prior success (CGPA). Addressing root causes is more effective than targeting motivation directly.