Figure 1: Interactive Power BI dashboard providing an overview of sales, profit, trends, and regional performance.
This project presents an end-to-end data analysis workflow focused on analyzing Superstore sales data.
The goal of the project is to explore sales performance, profitability, seasonal trends, and regional distribution using R for data preparation and Power BI for interactive visualization.
The project is designed as a junior data analysis portfolio project, demonstrating core analytical skills and clear business-oriented insights.
- Analyze overall sales and profit performance
- Identify profitable and loss-making product sub-categories
- Explore sales trends over time
- Understand regional sales distribution
- Build an interactive and easy-to-use dashboard
- R
- Data cleaning
- Date conversion
- Feature engineering (year and month variables)
- Power BI
- Interactive dashboards
- KPI design
- Data visualization (Bar, Line, Donut, Map)
- Slicers and filters
- Data Analysis Skills
- Trend analysis
- Profitability analysis
- Business insight generation
As a junior data analyst, I used AI-based tools as a learning and support resource during this project.
AI assistance was primarily used to help understand concepts, validate approaches, and improve code structure.
All data cleaning, analysis decisions, and dashboard design were reviewed, implemented, and interpreted by me to ensure full understanding of the workflow and results.
The following insights were derived from interactive filtering and visual exploration of the Power BI dashboard.
The Technology category accounts for the highest percentage of total sales, making it the primary revenue-driving category.
This suggests strong demand for technology products compared to other categories.
Figure 2: Donut chart showing the percentage contribution of each product category to total sales.
Sales data shows a significant increase toward the end of the first quarter, which is likely caused by a large deal or bulk purchase from a major customer or organization.
Additionally, sales increase again toward the end of the year, indicating seasonal demand that may be linked to promotions, budget cycles, or year-end purchasing behavior.
Figure 3: Line chart illustrating monthly sales trends and highlighting seasonal patterns and sales fluctuations.
The profit and loss analysis reveals that not all high-selling sub-categories are profitable.
Some sub-categories consistently generate losses, highlighting potential issues related to pricing, discounts, or shipping costs.
This insight helps identify areas that require further cost or pricing review.
Figure 4: Bar chart comparing profit and loss across product sub-categories, highlighting profitable and loss-making segments.
The map visualization shows a high concentration of sales in the Eastern and Northern regions.
This is likely due to the presence of large cities, which attract higher population density and purchasing activity.
In addition, easier and faster delivery to nearby urban areas contributes to higher order volumes.
Figure 5: Geographic map visualizing sales distribution across regions, with higher intensity in densely populated areas.
- Review pricing and discounts for loss-making sub-categories to ensure positive profit margins.
- Optimize shipping and logistics costs, especially for products with high delivery expenses.
- Focus on profitable categories and regions by prioritizing inventory and targeted marketing efforts.
- Use the Region slicer to analyze performance by geographical area
- Use the Year filter to explore trends over time
- Hover over charts to view detailed values
- Combine filters to perform focused regional and temporal analysis
This project demonstrates a complete data analysis process, starting from data cleaning and preparation in R and ending with an interactive dashboard built in Power BI.
The analysis provides clear insights into sales performance, profitability, and regional behavior, supporting data-driven business decisions.
👤 Mohammed Elmojtaba
📧 Email: mohammed.elmojtaba@hotmail.com
💼 LinkedIn: [https://linkedin.com/in/mohamedelmojtaba]
🐙 GitHub: [https://github.com/M-elmojtaba-code]
Feel free to connect with me for any inquiries or data collaborations!