This project is all about digging into some Amazon sales data and making sense of it through solid exploratory data analysis. I used Python and a bunch of its data tools (mainly pandas, matplotlib, and seaborn) to clean the data, explore what’s going on, and highlight the most interesting parts.
First, I cleaned the raw data, there were some missing values, inconsistent formats, and a few things that needed fixing to make the analysis smoother.
After that, I started breaking down the data:
- Looked at which product categories were getting the most attention.
- Analyzed sales across different marketplaces to see if any stood out.
- Checked out seller performance and ranked the top 3 sellers in each category, focusing specifically on January.
- Used visualizations to bring the numbers to life and make the insights easier to spot.
- Some product categories clearly dominate the sales, no surprise, but it’s good to see it in the data.
- A few sellers really outperformed the rest in their categories.
- There are patterns depending on the marketplace and time period that could help businesses make smarter decisions.
This project was a great chance to apply EDA skills on something practical. If you're into data analysis or just curious about e-commerce trends, you'll probably find something interesting here.
E-commerce platforms like Amazon generate a massive amount of transactional data. Being able to extract useful insights from such data is a key skill in data analysis and business intelligence. This project is a hands-on example of applying those skills on real-world-like data.
Hope you enjoy going through the project and maybe even learn something new from it.
If you found it helpful, feel free to leave a star it means a lot and keeps things going.