Minor Machine Learning Classification Project -- 2

I have a small dataset in a clean CSV and I need a concise end-to-end classification solution built around it. The data are purely numerical, so there’s no feature extraction headache—just sensible preprocessing, model selection, training, and evaluation.

Here’s what I expect: you load the CSV, handle scaling or other standard prep, split the data, choose and justify an appropriate algorithm (anything from logistic regression to tree-based models is fine as long as performance is solid), then deliver a trained model plus the Python code or notebook that reproduces the entire workflow. A short report or markdown cell summarising accuracy and key metrics will round it off.

If you can wrap the model with a neat predict() function so I can drop in new rows later, even better. I’m aiming for something clear and lightweight rather than enterprise-grade complexity, so please keep external dependencies minimal—scikit-learn and pandas should do the trick.

Acceptance criteria:
• Reproducible script/notebook that runs without errors
• Accuracy, precision and recall printed after training
• pickle or joblib file of the fitted model
• Brief explanation of any hyperparameter tuning choices

Let me know your estimated turnaround and we’re good to go.