Dashboard
🛡️ Admin
🔔
AD
Total Students
👥
0
Analysed records
Predicted Dropouts
⚠️
0
Flagged by model
High Risk
🚩
0
Need intervention
Dropout Rate
📈
0%
Of analysed students
Predicted Outcome
Risk Distribution
Avg Dropout Probability by Age Group
Student Details
Enter what you know — the rest uses dataset averages.
📊
Fill the form and run a prediction to see the result here.
0%risk
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Top Contributing Factors
🧠 See How the Machine Thinks
Enter a student's details and watch the two models compute the dropout risk, step by step — Random Forest lets many decision trees vote, while XGBoost builds the answer one boosting round at a time. The numbers below are the model's real internal calculations.
Student Details
Enter what you know — the rest uses dataset averages.
🧠
Enter details and press Run Visualization to watch both models work.
① Features
② Forest
③ XGBoost
④ Verdict
Input features fed to the models
🌳 Random Forest — 200 trees each cast a vote
Forest dropout risk (average of all trees): —
⚡ XGBoost — risk refined over 400 boosting rounds
Each round adds a small correction. Watch the predicted risk converge.
XGBoost dropout risk: —
Final Verdict
🌳 Random Forest
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⚡ XGBoost used
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At-Risk Student List
| ID | Name | Age | Gender | Scholarship | Fees OK | Sem1 | Sem2 | Probability | Risk | Actual |
|---|
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