This project performs an Exploratory Data Analysis (EDA) on the Superstore Sales dataset to uncover insights about customer segments, sales, profit distribution, and discount impacts. The analysis highlights regional performance, product profitability, and sales-to-profit ratios across categories.
The project focuses on:
- Understanding which categories and sub-categories drive sales and profit.
- Evaluating customer segments and their contribution to revenue.
- Identifying loss-making products due to discounts.
- Comparing regional performance in terms of sales and profit.
The dataset used in this analysis is Superstore.csv.
It contains 9,994 rows × 21 columns with the following details:
- Order details: Order ID, Order Date, Ship Mode
- Customer details: Customer Name, Segment
- Geography: Country, City, Region
- Product info: Category, Sub-Category, Product Name
- Financials: Sales, Quantity, Discount, Profit
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Which category generates the highest sales and profit?
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Analyze sales based on product categories and determine which category has
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The sales analysis needs to be done based on sub-categories.
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You need to analyze the monthly profit from sales and determine which month had the highest profit.
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Analyze the profit by category and sub-category.
- Analyze the sales and profit by customer segment
- Analyze the sales to profit ratio
The notebook includes:
- Bar plots for category and segment performance.
- Pie charts for sub-category contribution.
- Histograms for profit and discount distribution.
- Heatmaps for regional insights.
- Scatter plots for sales vs. profit.
- Python
- Pandas → data handling
- NumPy → numerical computations
- Plotly → interactive visualizations
- Jupyter Notebook → analysis environment
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Clone the repository or download the notebook.
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Install dependencies:
pip install pandas numpy plotly notebook
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Place the dataset (
Superstore.csv) in the same folder as the notebook. -
Open the notebook:
jupyter notebook ef5b7590-79c6-451a-9e7f-fd722ddf743f.ipynb
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Run the notebook cell by cell.
- Technology is the top-selling category but not always the most profitable.
- Consumer segment generates the largest share of revenue.
- Tables category often incurs losses due to high discounts.
- Western region leads in overall sales.
- Profitability is highly dependent on discount strategy.
- Build a dashboard (e.g., with Power BI or Streamlit).
- Create a predictive model to forecast sales and profit.
- Apply clustering to segment customers based on spending.
👨💻 Author: Anand Saundarya 📅 Date: September 2025