Rosalís Amador García

Rosalís focuses on data and machine learning, with a small set of Jupyter-based repositories and a modest following of four. The footprint is thin — two original repos, no stars, no named projects — but the abandonment ratio is lower than most peers here, hinting at slightly steadier engagement. There are no commits in the past year and no verified external contributions. Read fairly, this is an emerging data/ML practitioner with a clear notebook-driven focus who is still assembling a public body of work. The direction is coherent; the evidence base is small.