Simple Numerical Data Classification

I have a small, well-structured numerical dataset that needs a clean, reliable classification model. The task is deliberately scoped to be quick: load the data, explore it briefly, build and evaluate a baseline model (I’m open to anything from logistic regression to tree-based methods or lightweight neural nets), then hand back reproducible code and a concise report of the results.

I value clarity over complexity—well-commented Python notebooks or scripts, tidy plots, and an explanation of why you chose a particular algorithm will go a long way. Please share examples of past work that demonstrate you can keep things organised and interpretable; that portfolio piece will help me decide fast.

Ask me any questions you need before you dive in, and feel free to propose the tools you’re most comfortable with (scikit-learn, TensorFlow, PyTorch, etc.). I’m ready to get started as soon as we agree on the approach.