Customer Purchase Behavior Insights

I have a sizeable customer-level transaction dataset covering the past two years, and I need clear, story-driven descriptive analysis focused on purchase behavior. The raw CSVs already sit in a cloud folder; they contain order IDs, customer IDs, product codes, timestamps, quantities and net revenue.

Your job is to explore and summarise this information so I can quickly answer questions such as:
• How often do customers buy and what is the typical basket size?
• Which products or categories dominate repeat purchases?
• What seasonality or time-of-day patterns emerge?
• How does average order value shift across segments (e.g., first-time vs repeat shoppers)?

Deliverables
1. A cleaned, well-documented dataset (Python script or SQL query included).
2. An executive-level report (PDF or slide deck) that walks through key findings, charts, and actionable insights.
3. An interactive dashboard (Power BI, Tableau, or a Jupyter Notebook with Plotly) so I can slice the data myself afterward.

Acceptance criteria
• All calculations are reproducible from the supplied code/notebook.
• Visuals label axes, units and sample sizes clearly.
• Commentary ties each metric back to a purchase-behavior question.

Feel free to use pandas, NumPy, Matplotlib or any other analytics stack you prefer; just keep the workflow transparent. Once you deliver the final assets, I’ll run a quick spot-check on a sample of rows to ensure totals reconcile with the source files before releasing the milestone.