Damiieibikun/Student-s-Dropout-Prediction-using-Supervised-Machine-Learning-Classifiers

A simple Data science project on Predicting Student's dropout using Machine Learning classification models

15
/ 100
Experimental

Implements ensemble classification techniques across Decision Tree, Random Forest, and Logistic Regression models trained on 11-year institutional datasets spanning demographic, academic, and socio-economic features, with scikit-learn and pandas pipelines for preprocessing. Identifies enrollment age and course selection as primary dropout predictors, achieving 83% accuracy with Decision Tree classification. Results delivered as reproducible Jupyter notebooks with matplotlib/Plotly visualizations for exploratory analysis and model performance evaluation.

No commits in the last 6 months.

No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 1 / 25
Community 8 / 25

How are scores calculated?

Stars

20

Forks

2

Language

Jupyter Notebook

License

Last pushed

Nov 14, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Damiieibikun/Student-s-Dropout-Prediction-using-Supervised-Machine-Learning-Classifiers"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.