Categorical Data Pattern Discovery

I have a dataset made up entirely of categorical variables and I want to understand the hidden relationships inside it. The task is strictly exploratory: I am not asking for predictive modelling, only a deep dive that surfaces meaningful patterns and trends.

What I expect from you
• Clean the data where needed so that the exploratory work is reliable.
• Use suitable techniques for categorical exploration—cross-tabulations, chi-square tests, association rules, clustering on encoded variables, or any other method you feel is insightful.
• Present the findings in clear, non-technical language supported by concise visuals (bar charts, heat-maps, mosaic plots or similar).
• Provide a short, well-commented notebook or script (Python with Pandas, NumPy, SciPy, scikit-learn or R equivalents) along with an executive summary slide or PDF.

Acceptance criteria
The deliverable should let me:
1. See the key relationships between categories at a glance.
2. Understand any notable trends or unexpected concentrations.
3. Walk away with two or three actionable insights I can share with stakeholders.

If you’re comfortable with exploratory data analysis and can turn categorical noise into a coherent story, I’d love to see your approach and timeline.