Abstract
We study prediction of superconducting critical temperature (Tc) from 81 composition-derived descriptors across 21,263 materials. To keep the analysis transparent and repro- ducible, we focus on linear models: Ordinary Least Squares (OLS), Ridge, Lasso, and Elastic Net (ENet). All models share a single evaluation protocol (5-fold cross-validation with standardized inputs) and are compared on RMSE, MAE, and R2. On this feature set, OLS attains the best cross-validated performance (RMSE = 17.6 K, MAE = 13.3 K , R2 = 0.735), with Lasso/ENet essentially tied next (RMSE ≈ 17.7 K , R2 ≈ 0.734); Ridge underperforms (RMSE = 18.9 K , R2 = 0.695). We report the full method, hyperparameter grids, diagnostic plots, and a minimal but complete set of metrics, aiming for clarity without overengineering.
Semester
Fall 2025
Course Name
STA 5703 Data Mining 1
Instructor Name
Dr. Emil Agbemade
College
College of Sciences
STARS Citation
Ahiduzzaman, Md, "Predicting Superconducting Critical Temperature from Composition-Derived Features: A Transparent Linear and Regularized Regression Study" (2025). Data Science and Data Mining. 47.
https://stars.library.ucf.edu/data-science-mining/47
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