I have a small, curated dataset of European songs and need a clear, reproducible demonstration model built in Python using TensorFlow/Keras. The goal is simply to show how we could predict or classify which tracks are most likely to resonate with European listeners—nothing production-grade, just a clean proof of concept that I can study and rerun. Here’s what I’m after: • A short, well-commented notebook or script that loads the data, performs any essential preprocessing, trains a straightforward model, and prints basic evaluation metrics. • Clear instructions (README or inline notes) so I can execute everything on my machine with a fresh virtual environment. • A brief write-up—one pager or a few slides—summarising feature choices, model architecture, and how to retrain or adapt the workflow to new data. I’m looking for European in the CET time zone and would like to exchange quick messages while you work, so please only respond if you can comfortably align with those hours. If you’ve built similar demos before and enjoy explaining your decisions in plain language, this should be a quick, enjoyable task. Thanks in advance, and I look forward to collaborating!