Python Classification Model from Excel

I have a dataset stored in one or more Excel sheets and need a complete machine-learning workflow in Python that turns those raw rows into an accurate classification model. Your job is to read the file(s), clean and prepare the data, pick a suitable algorithm, train, validate, and hand back code I can rerun on my own machine.

Please work in a Jupyter Notebook or a well-commented .py script, using familiar libraries such as pandas, scikit-learn, NumPy and matplotlib/seaborn for any quick visual checks. At the end I want to see performance metrics (accuracy, precision-recall, confusion matrix or similar) so I understand how well the model is behaving. If feature engineering or hyper-parameter tuning will noticeably improve results, feel free to add it and note the changes.

Deliverables:
• Clean, runnable Python code or notebook that loads the Excel data and performs the full classification pipeline
• A brief readme or inline markdown explaining each step and how to reuse the model with fresh data
• Saved model file (pickle/joblib) if relevant, plus any plots or reports generated during evaluation

That’s it—if you’ve built reliable classification models from spreadsheet data before, this should be straightforward.