Selecting suppliers is recognized as one of the key aspects in supply chain management (SCM). Choosing the right supplier is a challenging task within supply chain management, as it involves various criteria and decision-making methods. Supplier selection is based on the suppliers’ characteristics. Although many features are discussed in the literature, managing all features can be difficult. To identify the best suitable set of features so that accurate results are obtained when applying a machine learning algorithm to the selected attributes. It is important to identify relevant features to obtain accurate results. The purpose of this article is to identify the optimal combination of supplier features which may be efficiently handled by machine learning algorithms (MLA), leading to the optimal results. Herein article principal component analysis (PCA) is utilized as a feature reduction algorithm. In PCA, a component refers to a new axis or direction in the feature space that maximizes the variance of data.