what is the-dosage-decider-decision-tree-patient-personalization-engine fr?

The Dosage Decider is an educational simulation written in Python that teaches how decision tree classification works by walking through a pretend medical dosing scenario. You play the role of a clinical pharmacy specialist building a flowchart style rule engine that looks at patient traits, such as age ranges and metabolic function scores, and uses them to decide on a dosing recommendation. The real point of the project is teaching, not actually giving medical advice. It walks through three core ideas from machine learning: decision trees as a way of splitting data into smaller, more uniform groups, Shannon entropy as a way to measure how mixed up or unpredictable a group of data is, and information gain as the method used to pick which question or split reduces that unpredictability the most. As you work through it, you can see the entropy score calculated for the starting group of patients, then see how splitting the data lowers that score. Gameplay involves inspecting a set of sample patient records already labeled with past dosing decisions, watching the entropy calculation happen for that starting group, comparing candidate splits by their information gain, and finally tracing a new patient's profile step by step through the resulting tree to reach a final dosing outcome. The project is intentionally dependency free, meaning it only uses core Python and basic math, with no external machine learning libraries. To run it you need Python 3 installed, then you clone the repository, save the provided script as dosage_tree.py, and run it directly from the command line. There is no installation of extra packages required.