This project performs Exploratory Data Analysis (EDA) on the Superstore dataset to uncover key business insights related to sales, profit, discounts, and customer purchasing behavior.
- Analyze sales performance across cities, categories, and sub-categories
- Identify the most profitable regions and categories
- Detect loss-making products and areas
- Understand the impact of shipping modes on profit
- Study the relationship between discount and profit
- The Technology category generates the highest profit
- The Furniture category shows losses despite high sales
- Standard Class is the most preferred shipping mode
- Higher discounts tend to reduce overall profit
- Certain sub-categories consistently incur losses
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Sample Superstore Dataset
The analysis highlights important business patterns and suggests that managing discounts and focusing on high-profit categories can improve overall performance.