Title: Customer Shopping Behavior Analysis (Python, SQL, Power BI)

Description:

Ran an end-to-end analysis of 3,900 customer shopping records to uncover what actually drives purchases, testing common assumptions about discounts, subscriptions, and age against the actual data rather than taking them at face value.

Cleaned the raw dataset in Python (handled null values, removed a fully redundant column, engineered an age_group segmentation field), loaded it into MySQL, and answered 13 business questions using SQL, including window functions (ROW_NUMBER, RANK with PARTITION BY) to rank products within categories and seasons, and CTEs to segment customers into new, returning, and loyal tiers before cross-analyzing payment behavior.

Key findings ran counter to the obvious assumptions: subscribed customers did not spend more than non-subscribers, discounts had no meaningful effect on review ratings, and Clothing led revenue in every single season without exception. Built an interactive Power BI dashboard with slicers for subscription status, gender, category, and shipping type to make these findings explorable.