model_name: online-course-performance-predictor license: cc0-1.0 language: en tags: - tabular - classification - education - student-performance - synthetic-data metrics: - accuracy - f1 - roc_auc

Online Course Performance Predictor

Model Overview

Online Course Performance Predictor is a machine learning model trained to predict whether a student will successfully complete an online course.
The dataset is fully synthetic, representing student engagement and learning behavior.
Intended for demos, research, and ML experimentation.

Model Details

Model Name: online-course-performance-predictor
Model Type: Binary Classification
Framework: scikit-learn / XGBoost
Input: Tabular CSV data
Output: Completion Status (Completed / Dropped)

Training Data

Synthetic dataset with the following features:

Feature	Type	Description
student_id	String	Unique student identifier
age	Integer	Student age
gender	Categorical	Gender
course_category	Categorical	Data Science, ML, Web Dev, etc.
enrollment_date	Date	Course enrollment date
progress_percent	Integer	Course progress in %
avg_weekly_hours	Float	Avg weekly study hours
completion_status	Binary	Target label (Completed / Dropped)

Intended Use

✅ Predicting student course completion (synthetic/demo)
✅ Educational ML tutorials
❌ Real-world academic decision-making

Evaluation Results

Metric	Score
Accuracy	0.87
F1-score	0.85
ROC-AUC	0.89

Limitations

Synthetic data only
Simplified student behavior
Not representative of real-world universities

Ethical Considerations

Model does not use real student data.
Misuse in real educational decisions could create unfair outcomes.

License

This model is released under the CC0 1.0 Universal License (Public Domain).

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support