Developing a Data-Driven Statistical Model for Accurately Predicting the Superconducting Critical Temperature of Materials using Multiple Regression and Gradient-Boosted Methods

Author(s)

Emil Agbemade, University of Central FloridaFollow

Keywords

superconducting critical temperature, multiple regression, gradient-boosted model

Abstract

This study focuses on developing a statistical model for estimating the superconducting critical temperature (Tc) of materials using a data-driven strategy. The study analyzed 21,263 superconductors and used a combination of multiple regression and gradient-boosted models to make predictions. The analysis included a descriptive analysis of the distribution of Tc, feature selection using the Backwards selection method, and model diagnostics. The results showed that the gradient-boosted method outperformed the multiple linear regression method with an RMSE of 12.01 and an R² value of 88.23 after fine-tuning its hyperparameters. The study concludes that the gradient-boosted method is an effective approach for accurately predicting Tc in superconducting materials.

Semester

Spring 2023

Course Name

STA 5703 Data Mining 1

Instructor Name

Xie, Rui

College

College of Sciences

STARS Citation

Agbemade, Emil, "Developing a Data-Driven Statistical Model for Accurately Predicting the Superconducting Critical Temperature of Materials using Multiple Regression and Gradient-Boosted Methods" (2023). Data Science and Data Mining. 2.
https://stars.library.ucf.edu/data-science-mining/2