I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classification metrics—whichever fits once you see the target variable. • The final, best-performing model saved in a reusable format (pickle/joblib or SavedModel). • A short read-me that explains: setup steps, how to retrain with fresh data, and how to call the model for predictions. I’ll provide the dataset and any domain notes as soon as we start. Keep the workflow reproducible (Python 3.x, virtual-env or conda environment file). Clean, commented code and a results summary slide or PDF will serve as acceptance criteria.