This project focuses on performing Exploratory Data Analysis (EDA) and Data Visualization on the Superstore dataset to uncover meaningful business insights.
The workflow includes:
- 🐍 Data analysis using Python (Pandas, Matplotlib, Seaborn)
- 📊 Interactive dashboard creation using Power BI
- 🔍 Identifying key business patterns in sales, profit, and discounts
- 🐍 Python
- 📦 Pandas
- 📊 Matplotlib & Seaborn
- 📈 Power BI
- 📥 Data loading and preprocessing using Python
- 🔍 Exploratory Data Analysis (EDA)
- 📊 Visualization using Matplotlib & Seaborn
- 📈 Dashboard creation in Power BI
- 🧠 Insight generation for business decision-making
- 📦 Total Quantity Sold: 38K
- 💰 Total Sales: 2.30M
- 📈 Total Profit: 286.40K
- 🎯 Average Discount: 0.16
- 📊 Sales by Category
- 💰 Profit by Region
⚠️ Loss-making Sub-Categories- 🚚 Ship Mode Usage
- 🔗 Discount vs Profit Relationship
- 🔥 Correlation Heatmap
- 💡 Technology category drives the highest sales revenue
- 📉 Tables sub-category contributes to the highest losses
- 🌍 West region generates maximum profit
- 🚚 Standard Class is the most preferred shipping mode
⚠️ Higher discounts negatively impact profitability- 📉 Furniture category shows lower profitability despite significant sales
- 📓 Jupyter Notebook (EDA & Python Visualization)
- 📊 Power BI Dashboard (.pbix)
- 📄 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.
- 💼 LinkedIn: [https://www.linkedin.com/in/waheed-mujtaba/]
- 📂 GitHub Repo: [https://github.com/Waheed-6907/CodeAlpha_DataVisualization]