I have a collection of raw data that needs to be transformed into clear, actionable insights. At this stage the brief is intentionally open: I want your guidance on the best combination of cleaning, exploratory analysis, visualization, and predictive modeling to achieve maximum business value. You will start by reviewing the datasets I provide (they may come as CSV/Excel exports, direct database pulls, or API responses). From there, outline the most appropriate preprocessing pipeline, explore key patterns, and—if the data supports it—build predictive models that can be used operationally. All work should be reproducible in Python using standard libraries such as Pandas, NumPy, scikit-learn, and, where helpful, visualizations produced with Matplotlib, Seaborn, or Plotly. If you prefer R with tidyverse equivalents, that is equally fine; just keep the workflow transparent and well-documented. Deliverables: • Fully commented scripts or notebooks • A cleaned, documented version of each dataset • Visual summaries of the main findings (static PNGs or an interactive dashboard in Tableau/Power BI/Plotly Dash) • A short read-me explaining methodology, assumptions, and next-step recommendations I am available for quick clarification sessions as you progress, and I’ll review milestones promptly so you can iterate without delay. Let me know your proposed approach, tools, and a realistic timeline, and we can get started right away.