This project analyzes customer shopping behavior using transactional data from 3,900 purchases across multiple product categories. The goal is to uncover insights into spending patterns, customer segmentation, product preferences, and subscription behavior to support data-driven business decisions.
- Total Records: 3,900\
- Total Features: 18
Customer ID, Age, Gender, Item Purchased, Category, Purchase Amount (USD), Location, Size, Color, Season, Review Rating, Subscription Status, Shipping Type, Discount Applied, Previous Purchases, Payment Method, Frequency of Purchases
- 37 missing values in the Review Rating column
- Checked null values, data types, and statistical summaries
- Renamed columns to snake_case
- Imputed missing Review Rating values using median per category
- Removed redundant promo_code_used column
- Age grouped into Young Adult, Adult, Middle-Aged, Senior (quartiles)
- Converted purchase frequency into numeric day intervals
- Gender-wise revenue analysis
- Discount vs high-spending customers
- Top-rated products
- Shipping type impact on spending
- Subscriber vs non-subscriber comparison
- Customer segmentation (New, Returning, Loyal)
- KPIs: Revenue, Average Spend, Customers
- Customer and product segmentation
- Discount and shipping insights
- Target loyal and subscribed customers
- Promote subscriptions to repeat buyers
- Focus on top-rated products
- Optimize discounts and shipping strategies
Python, MySQL, Power BI, Jupyter Notebook