I have gathered a sizable set of peer-reviewed medical-literature reports detailing observed side-effects for several chemotherapy agents and dosing regimens. My objective is to translate those narrative and tabular findings into an intuitive simulation model that helps me understand—and eventually visualise—the incidence, severity, and timing of adverse events. Here is what I need from you: • Cleanly extract the relevant side-effect metrics from the literature I supply, noting sample sizes and any confidence intervals the authors provide. • Build a stochastic simulation (Monte Carlo or another well-justified approach) that can run thousands of virtual patient courses, reproducing the variability seen in real-world data. • Output easily adjustable parameters so I can test alternative dosing schedules or drug combinations without rewriting code. • Provide concise documentation and a short walkthrough video or notebook demonstrating how to run the model and interpret its outputs. Python with libraries such as NumPy, SciPy, or SimPy is preferred for transparency, but I am open to other languages if you can justify the benefits. Code must be annotated, reproducible, and accompanied by a brief validation summary comparing simulated versus reported incidence rates. Upon delivery, I will confirm accuracy by checking that extracted data match the source articles and that simulation outputs align within the published confidence bounds. Once those acceptance criteria are met, we can discuss possible follow-on work to refine dosing optimisation.