Project Objective
This project demonstrates how to automate data analysis and dashboard creation using Python. It analyses the Superstore Sales Dataset, performs data cleaning, computes KPIs, builds visualizations, and automatically generates a professionally formatted Excel dashboard named Superstore_Dashboard.xlsx.
Note: The analysis in this project is performed only on the Excel dataset provided on this page.
Tools & Libraries Used
- Python
- Pandas — data loading, cleaning, and transformations
- Matplotlib & Seaborn — charts for exploratory analysis
- XlsxWriter — automated Excel workbook & embedded charts
- Jupyter Notebook — interactive development and documentation
Key Features
- Data Cleaning: Automatic detection and removal of duplicate rows and basic null checks.
- KPI Calculations: Total Sales, Total Profit, Overall Profit Margin, and Top Regions/Categories.
- Automated Visualizations: Sales by Region, Profit by Category, Profit vs Discount, Monthly Sales Trends.
- Excel Dashboard Generation: Script writes charts and formatted KPIs into a single Excel file.
Final Dashboard Preview
How to Run This Project
Follow the step-by-step tutorial here:
Prerequisites: Python
Ensure you have Python installed on your computer. This project was built using Python 3.8+, but any recent version of Python 3 should work.
Download PythonPython's installer typically includes pip, the package manager we'll use in the next steps.
Get The Project Files
Create a new folder on your computer (e.g., C:\Projects\Superstore). Download and place the following two files inside it.
(Note: Use the files you downloaded from the GitHub repository).
Install Required Libraries
Open a command prompt (Terminal on Mac/Linux, CMD or PowerShell on Windows), navigate to your project folder, and run the following command:
This will download and install all the necessary libraries for the project.
Launch Jupyter
In the same command prompt, run this command to start the Jupyter Notebook server:
This will automatically open a new tab in your web browser. From the file list, click on Superstore Dataset.ipynb to open the notebook.
Run the Code & Get Your Dashboard
Inside the Jupyter Notebook, go to the menu bar at the top and click Cell > Run All. This will execute the entire script.
You will see the code run from top to bottom. Upon completion, a new file, Superstore_Dashboard.xlsx, will be created in your project folder.
Conclusion
With this project you can automate the entire dashboard creation workflow — from data cleaning and analysis to delivering a polished Excel file with KPIs and charts. Try it on your own Superstore dataset and customise the visualizations to match your business needs.
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