This end-to-end exploratory data analysis project investigates over 2,700 transactions from a fictional pet store. Using Python (pandas, seaborn, matplotlib), I cleaned and analyzed the data to uncover trends in product sales, pricing, and customer preferences. Key insights revealed the best-selling product categories per pet type and pricing patterns that could influence marketing and stocking decisions.
This project simulates a real-world analytics task using a dataset called transactions-pet_store.csv, containing retail transactions for both cat and dog products. The project walks through the OSEMN framework: Scrub, Explore, and Interpret to deliver actionable business insights.
The business needs clarity on which products are driving the most sales, which ones carry higher price points, and how customer purchasing behavior varies by pet type (cat or dog). The data was messy, with missing values and inconsistent prices, which needed correction before any useful insights could be derived.
Sole Data Analyst