In addition, we have also provided a geolocation dataset that maps Brazilian zip codes to latitude and longitude coordinates, making it even more valuable for analysis.
Important Note: This dataset is based on real commercial data, but it has been thoroughly anonymized to protect privacy. Any references to companies and partners mentioned in customer reviews have been replaced with the names of the great houses from Game of Thrones.
Feel free to explore and analyze this dataset for various insights, and don't hesitate to reach out if you have any questions or need further information.
Context: This dataset was generously provided by Olist, the largest department store in Brazilian marketplaces. Olist connects small businesses from all over Brazil to channels without hassle and with a single contract. Those merchants are able to sell their products through the Olist Store and ship them directly to the customers using Olist logistics partners. See more on our website: www.olist.com
After a customer purchases the product from Olist Store a seller gets notified to fulfill that order. Once the customer receives the product, or the estimated delivery date is due, the customer gets a satisfaction survey by email where he can give a note for the purchase experience and write down some comments.
I tried to introduce Olist a bit above, but I have placed the details about the dataset below. You can access it from the link.
Olist Brazilian E-Commerce Dataset on Kaggle
I have conducted four analyses for the Olist dataset, each focusing on different aspects:
- Order Analysis: This analysis provides insights into order-related data.
- Customer Analysis: Explore customer-related trends and patterns.
- Seller Analysis: Gain valuable information about seller performance.
- Payment Analysis: Understand payment patterns and transactions.
I performed these analyses using PostgreSQL and organized all the SQL queries into separate files, named after their respective analysis.
You can access the SQL query files and the results of these analyses in the corresponding folders.
Additionally, I have created visualizations for each analysis using Power BI. You can find these visualizations in the files associated with the respective analysis names.
Feel free to explore the details and insights provided in each analysis to better understand the Olist dataset.
I'm delighted to have completed this project. Analyzing e-commerce data with SQL and finding solutions to real-world business problems has once again emphasized the importance of using
SQL. If you found this project useful and liked it, please don't forget to give my GitHub repository a star. This will help the project reach a wider audience and contribute to its further
development. Feel free to reach out openly for any feedback, suggestions, or questions. It can be a source of motivation for both you and me, helping us grow.
Happy data analysis!