Restaurant Customer Purchase Analysis

I have a very large customer dataset from my restaurant operations that I want explored in depth. The sole focus is on purchase-pattern insights—how, when, and what guests buy—so I can refine promotions, optimize inventory, and improve table turnover.

You’ll start by cleaning and structuring the raw files, then move into quantitative analysis using whatever mix of Python (Pandas, NumPy), SQL, or a BI tool such as Tableau/Power BI you prefer. I expect clear visualizations and a concise written summary that highlights actionable findings—think cohort trends, time-of-day spikes, day-of-week swings, and any unexpected correlations you uncover.

Deliverables
• Cleaned, documented dataset (CSV or database dump)
• Interactive dashboard or set of charts illustrating the key purchase patterns
• Executive-style report (PDF or slide deck) translating the numbers into recommendations

Acceptance Criteria
• Reproducible code or queries included
• All visuals labelled and easy to interpret
• Insights tied directly to data evidence, not assumptions

If you’ve handled restaurant or other high-volume transactional data before, let me know—otherwise, walk me through your approach to getting reliable results from big, messy datasets.