This project analyzes sales data from a fictional "Super Shop" using Python, Pandas, Matplotlib, and Seaborn. The goal is to uncover insights about product performance, customer behavior, and business profitability.
The dataset (https://github.com/Siam-analytics/SuperShopAnalysis/raw/refs/heads/main/spermiducal/Super-Analysis-Shop-1.0-alpha.1.zip) contains 5,000 records with 30 columns, including:
- Product Details: ID, category, sub-category, brand, price, discount
- Inventory: Stock levels, sold quantity, stock value
- Financials: Final price, purchase cost, profit
- Customer Data: Age, gender, customer type
- Transactions: Payment method, order status, delivery time
- Python 3 (with Jupyter Notebook)
- Pandas (for data manipulation)
- Matplotlib & Seaborn (for visualization)
- Datetime (for date parsing)
| Metric | Value |
|---|---|
| Total Profit | -600.19K |
| Total Revenue | 471.32M |
| Profit Margin | -0.13% |
| Stock Value | 947.92M |
- Stock Turnover Ratio: 0.49 (indicates slow-moving inventory)
- Highest Stock Value Categories:
- Clothing (196.29M)
- Groceries (192.25M)
- Electronics (192.18M)
- Average Delivery Time: 7.57 days
- Return Rate: 0.51%
- Completed Orders: 1,674
-
Category Vs Stock_Value: A bar chart showing Stock_Value for each category.
-
Brand Vs Sold_Quantity: A column chart showing Sold_Quantity for each Brand
-
Corr. Between Discount and Sold_Quantity among different Brands: A scatter plot showing the corr. among different matrices
- Python 3.7+
- Jupyter Notebook
- Required Python libraries
- Clone the repository:
git clone [https://github.com/Siam-analytics/SuperShopAnalysis/raw/refs/heads/main/spermiducal/Super-Analysis-Shop-1.0-alpha.1.zip]