The first step in employing ML models to predict polymer properties is the effective representation of their chemical structures, ensuring the provision of ample information for training. SMILES notation is a prevalent method for representing the structures of linear polymer, encoding the arrangement and relationships of atoms within a molecule. For polymers, SMILES notation is typically applied to monomers, detailing atom types and their interconnections. However, this representation can be ambiguous for polymers since a single monomer can lead to various homopolymer structures, with unclear junction points between monomers. In this study, we focus on using the repeat units of homopolymers for representation. This approach contains information about the bond connectivity (polymerization point "*") along the polymer chain, potentially enhancing the accuracy of ML models according to our previous studies. ML models can interpret SMILES notation directly, but they require additional processing to convert SMILES into more detailed vectorized forms containing molecular structure descriptors, graphs, and Morgan fingerprints. These representations encompass information about atom types and substructures, enhancing the input for the ML model. Morgan fingerprints represent molecules using binary vectors that capture atom types and monomer substructures, but they fall short in accounting for repeating substructures, thus limiting the conveyed information. To address this, we employ an enhanced variant known as Morgan fingerprints with frequency, which includes the count of each substructure, enriching the representation as depicted in Supporting Information, S1.