PythiaCHEM is organized into six modules, each encompassing specific functions accessible from Jupyter Notebooks (Table ). These modules are documented and structured based on their intended purpose, ensuring a user-friendly experience for both experienced and novice users. This approach keeps the Jupyter Notebooks uncluttered while providing users the option to add custom functions to the appropriate module. Notebooks are organized and named according to their aim, offering standard workflows for both classification and regression tasks (Table ). Users can readily tailor these workflows by adding or removing steps to align with their research needs. This structured approach provides a flexible framework for implementing various modules and functions included in the toolkit. From a CSV file, the appropriate features and algorithms are selected, and the models are trained and explained.