The author of this document has limited its availability to on-campus or logged-in CSUSB users only.

Off-campus CSUSB users: To download restricted items, please log in to our proxy server with your MyCoyote username and password.

Date of Award

12-2024

Document Type

Restricted Project: Campus only access

Degree Name

Master of Science in Computer Science

Department

School of Computer Science and Engineering

First Reader/Committee Chair

Sun, Qingquan

Abstract

The Automated Regression Analysis Application for Data Processing incorporates data processing, regression analysis, and visualizations. It’s meant to automate the process of making detailed analyses in a way that a non-technical person can understand. The application combines the waterfall method as a data engineering methodology to implement data processing and regression analysis. The application includes an easy-to-use interface design for a smooth user experience. This innovative application is written in Python and incorporates the following libraries: Pandas for data manipulation, Numpy for numerical operations, Matplotlib for data visualizations, Sklearn for implementing regression models and R2 scores, and Tkinter for user interactions. The application takes in data input and lets the user select a dependent and an independent variable for analysis. The dependent and independent variables are processed with multiple regression models. The best model is selected by the highest R2 score. A graph of the best regression model is displayed for analysis. A prompt for inputs for an independent variable that gives a predictive dependent variable based on input is also displayed. The application design is focused on being simple to use. During the development of the application, there was an emphasis on a smooth user experience throughout the series of computation tasks for non-technical users.

Download

Share

COinS