In catalysis, various experimental parameters can differ significantly in magnitude. For instance, the concentration of a reactant might be in parts per million (ppm), while the temperature could be in hundreds of degrees Celsius. Without scaling, a machine learning model might give undue importance to parameters with larger ranges. Min-max scaling ensures that all parameters contribute equally to the model, thereby improving its accuracy and reliability.