Date of Graduation
5-2025
Document Type
Thesis
Degree Name
Bachelor of Science
Degree Level
Undergraduate
Department
Data Science
Advisor/Mentor
Dr. Karl Schubert
Committee Member
Jamelle Brown
Second Committee Member
Justin Shipp
Abstract
With the rising popularity of data science and digital data storage, companies of many scales are looking to harness their data for decision making. Bentley Ave Data Labs, a data consultancy group, has sought to capitalize on this rising popularity by commissioning a low code machine learning platform. This thesis will explore the creation of a low code machine learning platform, intended to allow non-technical audiences to experience the power of data science.
The application will use Streamlit to allow for a user-friendly user interface and Python for the backend coding. The application will allow the user to clean, visualize, model, and interpret data from any CSV or Excel file. The models will be classification and regression based to accommodate a number of data types and include classification models such as Random Forest and Logistic Regression and regression models such as Polynomial Regression, Random Forest Regression, and Lasso Regression. The application detailed in this thesis will allow non-technical audiences to harness the power of data science and help push them towards data driven decisions.
Keywords
Modeling; Data Science Process; Data Cleaning
Citation
Timby, A. D. (2025). Breaking Down Data Science: A Low-Code Machine Learning Platform. Data Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/dtscuht/27
