I’m sitting on a sizeable finance dataset and need more than a retrospective look—I want clear, data-driven guidance on what to do next. The first step is a concise summary of the historical numbers so we can anchor the conversation; from there, the real focus is prescriptive analysis that translates those patterns into actionable recommendations. You’ll clean and explore the data, highlight the key historical insights, then build an optimization or scenario-based model that prescribes concrete actions (e.g., portfolio rebalancing, cost-cutting levers, revenue-boost initiatives). Python with pandas, NumPy, scikit-learn—or R with tidyverse—is fine as long as the workflow is fully reproducible and the logic is transparent. Deliverables • Cleaned dataset with documented preprocessing script/notebook • Visual and written summary of the historical performance • Prescriptive model code plus a brief, plain-English explanation of how it works • Final report outlining recommended actions, expected impact, and any scenario simulations Accuracy, clarity, and well-commented code are must-haves. If this matches your skill set, I’m ready to get started right away.