🎓 Student Dropout Prediction System

This AI-powered system predicts whether a student is at risk of dropping out based on demographic, academic, and socioeconomic factors.

Model: Random Forest Classifier | Accuracy: 92.01%

👤 Personal Information

Marital Status (1=single, 2=married, etc.)

Gender (1=male, 0=female)

Age at Enrollment

17 70

Displaced (1=yes, 0=no)

International Student (1=yes, 0=no)

Educational Special Needs (1=yes, 0=no)

🎓 Academic Background

Application Mode

Application Order

Course ID

Daytime/Evening (1=daytime, 0=evening)

Previous Qualification

👨‍👩‍👧 Family Background

Mother's Qualification

Father's Qualification

Nationality

Mother's Occupation

Father's Occupation

💰 Financial Status

Debtor (1=yes, 0=no)

Tuition Fees Up-to-date (1=yes, 0=no)

Scholarship Holder (1=yes, 0=no)

📚 First Semester Performance

Credited Units

Enrolled Units

Evaluations

Approved Units

Average Grade

0 20

Without Evaluations

📚 Second Semester Performance

Credited Units

Enrolled Units

Evaluations

Approved Units

Average Grade

0 20

Without Evaluations

📊 Economic Indicators

Unemployment Rate (%)

0 20

Inflation Rate (%)

-5 10

GDP

-10 10

📊 Prediction Results

📋 Example Students

Click to load example data

ℹ️ How it works

This model uses a Random Forest Classifier trained on data from 50,000+ students. It analyzes 34 different factors including:

📚 Academic performance (grades, units completed)
💰 Financial status (debtor, tuition fees)
👨‍👩‍👧 Family background (parental education and occupation)
📊 Economic indicators (unemployment, inflation, GDP)

Accuracy: 92.01% on test data

⚠️ Disclaimer: This is a predictive tool for educational purposes. Actual student outcomes depend on many additional factors.