New York State Public Schools Graduation Rates: Predicting Success.
The aim of this project is to model the way government expenditures and labor appropriation impacts secondary education graduation rates in New York State Public Schools.
The machine learning algorithms implemented are Elastic Net, SVR, Bayesian Ridge, AdaBoost Regressor, Random Forest Regressor, and neural network regression. Our experiments show that diminishing returns are not present in funding, rather the educational staff’s quality affects graduation rates.
Deliverables: Final Script & Report (https://github.com/mnovovil/GraduationRatePredictor1/blob/main/FinalReport.pdf), Final Presentation.
Dataset, Data Model & Data Dictionary Included.