I’ll hand over three raw datasets—sales transactions, customer demographics, and product inventory—spanning several stores. Your task is to stitch them together, clean inconsistencies, and delve into them with Python. Using Pandas, NumPy, and Matplotlib (feel free to add Seaborn or Plotly if that speeds insight), uncover how buying behaviour shifts: • weekday versus weekend • month by month I’m interested in concrete, data-backed stories: which products spike on Saturdays, whether certain customer segments shop more mid-week, seasonal category swings, ticket size trends, and anything else you spot that helps me fine-tune promotions and staffing. Deliverables • A merged, tidy dataset ready for future modelling • A well-commented Jupyter notebook showing the full analysis pipeline and visualisations • A concise PDF or slide deck highlighting key findings, charts, and actionable takeaways • A short README with environment details so I can reproduce results Acceptance Results must run end-to-end on a fresh environment (Python ≥3.9). All figures should be labelled and formatted for direct use in presentations. Insights need to reference specific metrics (e.g., uplift percentages, average basket size) so I can translate them straight into decisions.