a. Linear regression: A regression model (𝑦 " = 𝜔 # 𝑥 + 𝑏) predicts the melting point where "𝑥 " refers to the features, " 𝜔" is the coefficients of importance, "𝑏 " is the bias, and "𝑦 " " is the predicted melting point. The "mean absolute" difference between real and predicted values is used as the loss function. The model which minimizes this loss is considered the best-performing model.