I’ll hand you a raw CSV (or Excel) containing transactional records and would like it transformed into clear, actionable insights about customer purchase patterns. Here’s what I need: • Clean the dataset—handle missing values, fix data types, remove duplicates, and document any assumptions. • Explore and quantify key purchase-related metrics such as frequency, average basket size, and category preferences. • Highlight notable trends and segments in customer behavior, summarising them in plain language. • Present the findings visually, favouring bar charts for the main comparisons, though you can supplement with Matplotlib or Seaborn line or pie visuals if they help the story. • Compile everything into a concise PDF report or an annotated Jupyter notebook that walks through the code, insights, and conclusions. • Return the cleaned dataset alongside the report and image files of the charts. I’m comfortable with standard Python tooling (Pandas, Matplotlib, Seaborn) and will run or extend any notebooks you deliver, so please keep the code readable and reproducible.