This project focuses on performing Exploratory Data Analysis (EDA) and Data Visualization on the Superstore dataset to uncover meaningful business insights.
The workflow includes:
- 馃悕 Data analysis using Python (Pandas, Matplotlib, Seaborn)
- 馃搳 Interactive dashboard creation using Power BI
- 馃攳 Identifying key business patterns in sales, profit, and discounts
- 馃悕 Python
- 馃摝 Pandas
- 馃搳 Matplotlib & Seaborn
- 馃搱 Power BI
- 馃摜 Data loading and preprocessing using Python
- 馃攳 Exploratory Data Analysis (EDA)
- 馃搳 Visualization using Matplotlib & Seaborn
- 馃搱 Dashboard creation in Power BI
- 馃 Insight generation for business decision-making
- 馃摝 Total Quantity Sold: 38K
- 馃挵 Total Sales: 2.30M
- 馃搱 Total Profit: 286.40K
- 馃幆 Average Discount: 0.16
- 馃搳 Sales by Category
- 馃挵 Profit by Region
鈿狅笍 Loss-making Sub-Categories- 馃殮 Ship Mode Usage
- 馃敆 Discount vs Profit Relationship
- 馃敟 Correlation Heatmap
- 馃挕 Technology category drives the highest sales revenue
- 馃搲 Tables sub-category contributes to the highest losses
- 馃實 West region generates maximum profit
- 馃殮 Standard Class is the most preferred shipping mode
鈿狅笍 Higher discounts negatively impact profitability- 馃搲 Furniture category shows lower profitability despite significant sales
- 馃摀 Jupyter Notebook (EDA & Python Visualization)
- 馃搳 Power BI Dashboard (.pbix)
- 馃搫 Dataset (CSV)
This project highlights how data-driven analysis can uncover hidden business patterns and support strategic decision-making.
Managing discount strategies and focusing on high-profit categories can significantly improve overall business performance.
- 馃捈 LinkedIn: [https://www.linkedin.com/in/waheed-mujtaba/]
- 馃搨 GitHub Repo: [https://github.com/Waheed-6907/CodeAlpha_DataVisualization]