My internal sales database holds two years of transaction‐level records, and I want a clear picture of how customers actually buy. The objective is to understand user behavior by drilling into purchase patterns: frequency, basket composition, seasonal shifts, repeat-versus-first-time buyers, and any cross-sell or up-sell opportunities hiding in the numbers. You’ll receive a CSV export from our ERP that already contains order IDs, timestamps, SKUs, unit counts, prices, discounts, and anonymized customer IDs. I expect you to: • Clean and validate the dataset, documenting any data-quality issues found. • Perform exploratory data analysis in Python (Pandas, NumPy, Seaborn / Matplotlib) or R if you prefer, then build clear visualizations that highlight the patterns above. • Produce an executive-level slide deck (PDF or Google Slides) explaining key insights, plus an appendix with detailed code, charts, and summary stats so my tech team can reproduce the work. Acceptance criteria: insights are reproducible from the supplied notebook/script, visuals are legible for non-technical stakeholders, and recommendations are backed by statistically sound evidence (e.g., confidence intervals or significance tests where relevant). Please indicate which environment you’ll use (Jupyter, RStudio, etc.) and your estimated turnaround time.