  Copper matrix composite materials have exhibited a high potential in
applications where excellent conductivity and mechanical properties are
required. In this study, the machine learning models have been applied to
predict the hardness of the copper matrix composite materials produced via
powder metallurgy technique. Two particular composites were considered in this
work. From experiments, we extracted the independent variables (features) like
the milling time (MT, Hours), dislocation density (DD, 1/m2 ), average particle
size (PS,nm), density (gm/cm3 ) and yield stress (MPa) while the Vickers
Hardness (MPa) was used as the dependent variable. Feature selection was
performed by calculation the Pearson correlation coefficient (PCC) between the
independent and dependent variables. We employed six different machine learning
regression models to predict the hardness for the two matrix composites.
