I have a raw Foodpanda dataset and want to understand how each restaurant truly performs. Your task is to run a focused exploratory data analysis that zeroes in on sales and revenue figures for every restaurant in the file. I’m interested in trends such as top- and bottom-line growth over time, seasonality, outliers, and any correlations you uncover between order volume, average basket size, discounts, or other revenue-related metrics you choose to engineer. Python, pandas, NumPy, and visualization libraries like Seaborn, Matplotlib, or Plotly are all welcome—as long as the code is clean, reproducible, and well-commented. Please avoid drifting into customer-review or delivery-time angles; the spotlight must stay on revenue-based performance. When you apply, attach a concise yet detailed project proposal outlining your planned workflow, key questions you’ll probe, expected turnaround, and how you’ll present the insights. Deliverables • Fully annotated Jupyter notebook (or equivalent script) that cleans the data and walks through the EDA • Clear visualisations and summary tables highlighting restaurant-level performance insights • A short executive summary (PDF or slide deck) translating technical findings into business-ready takeaways I will supply the dataset as soon as we agree to start, and I’m happy to answer clarifying questions along the way.