Keywords
Multiple Linear Regression, Lasso Regression, Extreme Gradient Boosting (XGBoost)
Description
Superconductors have substantial practical applications, among which Magnetic Imaging Resonance Imaging (MRI) is most widely used in the health industry. Two significant problems have prevented superconductors from being widely used: the first problem is that it is impractical to cool down the superconductors to extremely low temperatures of 77K, and the second problem is that the scientific model to predict the critical temperature (Tc). This project aims to find a scientific model to predict critical temperature more accurately.
Abstract
This paper focuses on utilizing multiple linear regression, lasso regression, and extreme gradient boosting algorithms to predict the critical temperature of the superconductor. The model will be evaluated using the mean square error and adjusted R-squared values, and the best model will be recommended for future work related to this study.
Semester
Spring 2023
Course Name
STA 5703 Data Mining 1
Instructor Name
Xie, Rui
College
College of Sciences
STARS Citation
Dhakal, Pradip, "Machine Learning-based Approaches for Predicting the Critical Temperature of Superconductor" (2023). Data Science and Data Mining. 9.
https://stars.library.ucf.edu/data-science-mining/9
Included in
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