This is the final project for Pacmann’s “Data Wrangling and SQL” course. Exploratory data analysis (EDA) will be used in this project to better understand Olist’s operation and identify problems. Finally, propose a solution to the problem and weigh the benefits. The Python programming language is used to analyze data in this project, and the seaborn library is used to visualize diagrams.
This is a public Brazilian e-commerce dataset of Olist Store orders. The dataset has information on 100,000 orders placed on Brazilian marketplaces between 2016 and 2018. Its features enable you to view an order from multiple perspectives, including order status, price, payment, freight performance, customer location, product attributes, and finally, customer reviews. We also published a geolocation dataset that associates Brazilian zip codes with latitude and longitude.
- How are orders growing from quarter to quarter?
- When placing an order, how does customer behavior change depend on the day and time of day?
- Is the shipping process proceeding as expected?
For a more comprehensive understanding of the results and methodology, we suggest referring to the medium article. The article provides an in-depth analysis and explanation of the findings and provides a comprehensive overview of the research process.