Analyze Product Purchasing Patterns

I have a raw export of our product catalogue, sales history, and click-stream events that track how shoppers move through the site. I need this data cleaned, merged, and explored so I can clearly see customer purchasing patterns: which items are most often bought together, sequence of product views that lead to conversion, time-of-day or day-of-week spikes, and any standout cohorts that behave differently.

You will start with a CSV dump (product attributes, prices, inventory) and a separate log of behavioural events (view, add-to-cart, purchase). After checking data quality and handling missing values, please build descriptive statistics and visualisations, then create more advanced analyses such as market-basket or association rules, RFM segmentation, and trend forecasting. Feel free to use Python (pandas, scikit-learn), R tidyverse, SQL, or a BI tool like Tableau/Power BI as long as the workflow is reproducible.

Deliverables:
• A cleaned, well-documented dataset (or SQL tables)
• An analysis notebook or script with commented code
• Interactive or shareable dashboards/charts that highlight purchasing patterns
• A short written summary translating key findings into plain-language recommendations I can act on immediately

I’ll provide sample records and field descriptions once we start, and I’m available for quick feedback loops so you can iterate rapidly toward the insights that matter most.