This project leverages the AI Development Workflow to predict the risk of student dropout in online learning platforms. It combines data science, machine learning, and software engineering practices to provide a CLI tool and reproducible training pipeline.
🔍 Predict which learners are at risk—before they drop out.
| Folder / File | Purpose |
|---|---|
data/ |
Sample training dataset in CSV format |
src/ |
Training, preprocessing, and evaluation scripts |
model/ |
Command-line predictor script (predict.py) |
notebook/ |
Model development and experimentation notebook |
report/ |
Case study, analysis, and deliverables |
requirements.txt |
Python dependencies for project environment |
README.md |
This project guide and usage manual |
Dropped Out / Continued)Run from inside the model/ directory:
```bash python predict.py –gender F –age 24 –language English –location Suburban –time_spent 5.5 –quiz_score 80 –login_count 9
| Feature | Description |
|---|---|
gender |
Student’s gender (M/F) |
age |
Age in years |
language |
Primary language of instruction |
location |
Region type (Urban/Suburban/Rural) |
time_spent |
Average weekly hours spent |
quiz_score |
Average quiz score (0–100) |
login_count |
Weekly login frequency |
recovery-main branch is currently ahead of main🪄 Future Ideas • [ ] Streamlit web app for real-time prediction • [ ] Model evaluation dashboard • [ ] Support for external CSV uploads • [ ] Add dropout probability (% confidence)
Made with passion, Python 🐍, and persistent debugging 💥
Leonard Phokane
Cloud Engineering · App Development · Ethical AI
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📊 AI Student Dropout Prediction