Francesco Stara
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?