Figure illustrates the most common approach for compositional features used in machine learning for chemistry and materials science. Often, no preprocessing (e.g., index normalization) is better than preprocessing. Chemical information such as structural complexity can be lost when opting for atomic % instead of the indices to represent chemical composition. However, simple preprocessing such as meaningful sorting and rearranging of formulas can greatly enhance model performance. Excel file formats and includes a filtering option that summarizes dataset content and filters data based on the number of elements in a formula or removes non-elements. Additionally, a heatmap based on the element occurrence can be generated, allowing users to visually analyze their dataset. If data is stored as CIFs in a folder, CAF can extract compositions from the CIFs and generate a table with element formulas. Following filtering, the second option is sorting, which can be based on composition (indices or element fractions). Another sorting method is based on properties; if a file containing properties is provided, they will be listed to give users the option to sort them in ascending or descending order. Sorting can also be based on a manually modified list of element groups to meet specific user needs. Once the file is updated with sorted compositions, the third option, featurization, can be applied using a pre-prepared list of descriptors designed to avoid mathematical operations that could result in values of infinity or NaN. The descriptor list can also be tailored to address specific problems the user aims to solve. For instance, we include the option for users to hot-encode their data, converting categorical information into a binary vector format suitable for machine learning algorithms. The presence or absence of an element is indicated by a 1 or 0, respectively. To maximize data utility, we have prepared binary and ternary featurizers, along with a universal featurizer that is agnostic to the number of elements in a compound. The final two options allow users to cross-reference the list of compounds against the folder containing CIFs and to enhance the file with features from other files (e.g., those generated by other featurizers).