Supervised Machine Learning Model Development

I need a fully working supervised learning model built from scratch, trained on my data, and delivered with clean, well-commented code. Although the focus is supervised learning, I also want to explore a clustering approach for initial data exploration and feature engineering, so the workflow should accommodate both phases gracefully.

Here’s what I expect:

• A brief, plain-English plan outlining the supervised objective (classification or regression, as appropriate once we review the dataset) and how clustering will support data insights.
• End-to-end code in Python, preferably using scikit-learn, pandas, NumPy, and (if deep learning proves beneficial) TensorFlow or PyTorch.
• Reproducible training pipeline: data loading, preprocessing, feature engineering, model training, hyper-parameter tuning, evaluation, and final model export.
• A concise report or notebook explaining metrics, feature importance, and any clustering findings.
• Clear instructions so I can rerun or extend the work locally.

I value tidy, modular code, meaningful commit messages, and frequent checkpoints so we can iterate quickly. If this fits your expertise, let’s get started right away.