As a cyber security specialist looking to expand my data analysis skills, I developed this project to gain hands-on experience with Python's data analysis features. E-commerce data presents real-world challenges including data cleaning, aggregation, and trend identification.
The dataset I analyzed is the "Online Retail" dataset from the UCI Machine Learning Repository from Kaggle. This dataset contains over 500,000 transactions from a UK-based online retailer between 2010-2011. It includes information about products, quantities, prices, customers, and invoice dates.
Question 1: What are the top 5 best-selling products by total revenue?
PAPER CRAFT, LITTLE BIRDIE, REGENCY CAKESTAND, WHITE HANGING HEART T-LIGHT HOLDER, JUMBO BAG RED RETROSPOT
Question 2: What are the monthly sales trends throughout the year?
Average monthly revenue: £685,492.92
Python, Visual Studio Code, Pandas data library, Matplotlib visualization library
- Add analysis for customer behavior
- Implement georgraphical filtering
- Create a dashboard using Plotly