In this e-commerce analysis project, I embarked on a comprehensive examination of online transactional data. Utilizing Python for preliminary exploration, SQL for in-depth investigation, and Power BI for detailed visualization, this work showcases a meticulous blend of data analytics and visual representation.
I first started analyzing the raw data with Python for:
1. Handling missing data
2. Handling cancelled transactions
3. Handling outliers
With the SQL I wanted to provide the client a good toolbox of queries that can be used for future analysis.
My goal is to inspect sales trends, discover high-performing products, learn about customer segmentation, and uncover trends in canceled transactions.
Some of the key issues to be addressed include: How have sales trends changed over the months? Which products are frequently purchased? How do cancellations impact overall sales?
As requested by the client I created a dashboard to transform analytical findings into intuitive and informative visuals, aiding in better data interpretation. What to expect?
Sales Trajectory: I showed the sales trends over time, offering a panoramic view of business performance.
Top-Performing Products: Visualization tools highlighted the products driving a good number of sales.
Customer Segmentation: Visual tools allowed for an in-depth comparison of regular versus occasional shoppers.
1 like · 29 views · Feb 24, 2024
This report presents a comprehensive study of a year's worth of e-commerce transactions, highlighting the patterns discovered.
