It is common to have a small training size of 50 to 1,000 training data in materials informatics. While these numbers can be considered limited, a well-prepared x-block component can be used for ML for materials property prediction. The Oliynyk property list, consisting of 93 elemental compositional-derived features, was used to propose the 1:3 crystal structure of a novel intermetallic component, UCd3 with SVM Discriminant Analysis, which was predicted to be Cu3Au-type. The proposed crystal structure was experimentally confirmed with the X-ray powder diffraction method after synthesis. A regression ML algorithm, Gaussian process regression (GPR) with the RBF kernel, was used to further enrich the Oliynyk property database by interpolating 26 radii values in 5 size scales and 17 electronegativity values in 3 EN scales. It is important to keep in mind that rarely a specific column in the x-block is the solution, rather an appropriate combination of the columns in the x-block carries the solution within. Furthermore, a combination of intertwined variables can help researchers guide and discover which set of properties govern the physical property of a compound. While there are other data-driven methods, not limited to