Development of a Web-Based Regression and Classification Learning Tool
Data Science — 2025, Undergraduate
web applicationregressionclassificationstatistical learning
This study developed a web-based regression and classification learning tool that guides users through fitting, selecting, and interpreting models while checking assumptions. The tool exposes metrics and residuals and warns when models misbehave. Adopting a client-server architecture and a survey-based usability evaluation, the system was built using Python, FastAPI, Scikit-learn, and PostgreSQL. The findings showed that the tool deepened learning and reduced analytical errors, while achieving a high System Usability Scale score. The study recommends adoption in statistical learning courses.
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