Author: Justin Bell
This project is an interactive data visualization built using D3.js that explores Amazon’s 2025 sales data. The dataset was sourced from Kaggle and contains detailed information about customer purchases, including order dates, product categories, total sales, customer locations, and payment methods. The main objective of this project was to design visualizations that enable users to explore patterns in sales performance, customer behavior, and transaction trends. This work reflects both a technical understanding of D3 and a data analyst's approach to uncovering insights through thoughtful visual design.
I began by reviewing several open-source datasets before selecting this Amazon sales dataset due to its structure and the number of useful attributes it contained. After identifying key variables such as sales, categories, dates, regions, and payment methods, I sketched out potential visual layouts to guide my design. I prioritized user interaction and readability, structuring the visualizations into clear sections with support for dropdown filters, tooltips, and annotations.
The first visualization I developed was a bar chart that presented total sales by product category, allowing a high-level view of revenue distribution. As I became more comfortable with D3, I expanded the dashboard to include a line chart of daily sales, a pie chart of payment methods, and other charts such as stacked bars and grouped bars. Each visualization was built as a modular function, allowing me to maintain clean code and add enhancements like tooltips and transitions iteratively.
I chose a dark background theme with high-contrast chart colors to enhance readability and visual appeal. Bar charts were used where exact comparisons were useful, line charts for temporal trends, and pie/stacked charts to represent proportional breakdowns. The dropdown filter by region allowed users to dynamically change the dataset and explore local trends across all charts. Interactivity such as hover tooltips and animated transitions was included to improve the user experience and help surface exact data values in a clean, non-intrusive way.
Through this visualization, I was able to identify that the majority of revenue came from Electronics and Home Appliances, while categories like Books and Clothing contributed significantly less. The sales over time revealed noticeable spikes, suggesting promotional events or seasonal behavior. Additionally, PayPal emerged as the dominant payment method, with relatively few transactions using Amazon Pay or Debit Card. This project helped reinforce the importance of visual design in data storytelling and gave me hands-on experience with parsing, cleaning, and presenting structured data in a browser-based environment.
Below is a short walkthrough video demonstrating how to use the interactive dashboard and explaining key findings: