I will share six months of billing, seating and order-channel logs so you can dig into how our guests actually behave. My main objective is to understand customer preferences, with a special lens on which order types—dine-in, takeaway or delivery—show the strongest demand trends. Along the way, I need the analysis to surface what is and isn’t working in our favour: winning dishes, under-performing items, the seating zones people gravitate toward, average revenue per guest, and how discounts are really influencing spend. Python is the stack we’re comfortable with, so feel free to lean on Pandas, NumPy, Seaborn, Matplotlib or Plotly; if a quick dash of scikit-learn helps uncover patterns, all the better. The final hand-off should combine: • Clear, well-labelled visualisations (think heat maps for seat popularity, trend lines for order channels, bar charts for menu performance). • Concise written explanations beside each chart so that decision makers grasp the “so what?” instantly. • A slide-style report summarising your findings and actionable next steps—menu tweaks, seating adjustments, promotional ideas—ranked by potential impact. Keep the code reproducible and commented; I’ll need to rerun it when fresh data comes in.