Customer Behavior Data Analysis

I need a concise, end-to-end Python workflow that turns a small customer transaction & engagement dataset into clear insights about how people behave on our platform. The data will require thorough cleaning and validation, thoughtful feature engineering, and sensible normalization before any analysis begins.

Once the data is tidy, please explore it with Pandas and NumPy, surface the most useful behavior patterns, then build a light predictive component focused on customer segmentation. A handful of well-chosen Matplotlib charts should illustrate the key trends and support the final narrative.

Deliverables
• A single, well-commented Python script or Jupyter Notebook that runs start to finish without manual tweaks
• The cleaned, processed version of the dataset saved back to disk
• Descriptive statistics plus a basic segmentation model (e.g., k-means or another suitable method) with explanation of why it was chosen
• 3–5 clear visualizations that highlight standout patterns and segments
• A brief written summary outlining the preparation steps, core findings, and any recommendations that follow from them

Keep the code modular, easy to follow, and limited to the standard data stack (Pandas, NumPy, Matplotlib). This is a small fixed-budget job, so efficiency and clarity matter just as much as accuracy.