Combination of various features from different sources and generated using various approaches is important. We provided a tool that besides generating our features allows users to integrate features from other sources by combining data matrices. The most important role in explainability plays preprocessing and tailoring dataset and features to a specific problem, such as organizing structure/formula in a meaningful way and introducing novel features, such as CIF radius. We built open-source command-line-based Python programs known as Composition Analyzer Featurizer (CAF) and Structure Analyzer Featurizer (SAF). These featurizers produce well-balanced and superior quality features readily available to be applied in machine learning models or used for classical structural map plotting. CAF generates numerical compositional features from a list of formulas provided in an Excel file, while SAF extracts numerical structural features from a .cif file by generating a supercell. To expedite the current state of machine learning and make sure it is more accessible, we kept in mind that most users may not have a computational background and therefore included that subjective experience when developing our software. We also looked at various solid state applicable featurizers already in use to provide a benchmark for ourselves and the reader. To emphasize the needs of the user, we ensured our software checked every box, and included various options within filtering, sorting, checking, and providing multiple visualizations for the user to have the smoothest experience possible while maintaining scientific knowledge. For validation, 133 features from CAF and 94 features from SAF were either combined or used separately to classify structures in equiatomic AB intermetallics. From the explainable model, a novel size scale CIF radius, various structural features, such as distortion of polyhedra, and data preprocessing were found to be important. The performance, measured in terms of precision, recall, F1score, and accuracy, was comparable to and surpassing those generated using features from JARVIS, MAGPIE, mat2vec, and OLED in PLS-DA and SVM. The combination of CAF and SAF showed promising results in addressing these challenges, suggesting potential for enhanced performance in crystallographic problem-solving tasks compared to other featurizers. Further research should focus on optimizing the integration of CAF and SAF, to fully realize their potential in improving performance and efficiency in these crystallographic systems.