Date of Award
5-2025
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Jin, Jennifer
Abstract
There is a heavy reliance on programming when it comes to learning machine learning (ML). This often creates barriers for students and newcomers unfamiliar with coding. While the lessons you learn in the classroom provide essential foundational understanding, some technical or practical aspects of ML—such as data preprocessing, feature engineering, and model tuning—are best learned through hands-on interaction. ML Playground was developed to act as a proof-of-concept application to address this gap by offering a browser-based, graphical user interface that lets users engage with core ML workflows without writing code. Designed with educational accessibility in mind, the application allows users to modify datasets, apply preprocessing techniques, perform feature engineering, and simulate machine learning models solely through an intuitive interface.
ML Playground provides real-time feedback on how user-controlled changes affect model performance, using metrics such as Mean Squared Error to guide exploration and encourage user experimentation. Built entirely in Python using Streamlit, ML Playground is extremely modular, enabling future extension to support additional models and techniques. While the scope of the current version is limited, it successfully demonstrates how simplifying the ML pipeline through a visual, codeless alternative can lower the barrier to entry and foster understanding and curiosity among learners. The application is ultimately aimed at bolstering traditional ML education with a beginner-friendly environment for experimentation and demonstrations of practical ML controlled entirely by the user. ◀
Recommended Citation
Cerrato, Marco D., "ML PLAYGROUND: DATA MODIFICATION/PREPROCESSING AND MODEL SIMULATION TOOL" (2025). Electronic Theses, Projects, and Dissertations. 2131.
https://scholarworks.lib.csusb.edu/etd/2131
Included in
Data Science Commons, Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Software Engineering Commons
