Prediction study on critical temperature (C) of different atomic numbers superconductors (both gaseous/solid elements) using machine learning techniques

Abstract Superconductors has been comprehensively studied as huge research effort taking into consideration of actuality that its invention ruins a theme of passionate discuss once its discovery completed. The standard behind this paper is the study about explaining as well as scrutinizing how different regression methods are used for predicting the superconducting critical temperature Tc from Superconductors database collected from Kaggle dataset source. Mainly, regression models such as linear Regressor, decision tree Regressor, Lasso Lars Regressor, Bayesian Ridge Regressor, XGB Regressor, and Huber Regressor have been studied to forecast critical temperature in superconductor materials.