In this project, we have aimed to analyse the sales data of the super store and provide useful insight into customer preferences.
The dataset used for this analysis is an excel file containing the following columns:
- Row ID
- Order ID
- Order Date
- Ship Date
- Ship Mode
- Customer ID
- Customer Name
- Segment
- Country
- City
- State
- Postal Code
- Region
- Product ID
- Category
- Sub-Category
- Product Name
- Sales
- Quantity
- Discount
- Profit
The analysis was conducted in Python using the following libraries:
- Pandas
- Numpy
- Matplotlib
- Streamlit (for deployment)
- The stores makes the most revenue in terms of sales from the state of California, followed by New York, Texas, Washington and Pennsylvania in that order.
- Over 50% of the store's revenue comes from consumer goods, followed by corporate and home-office in that order.
- The store offers multiple types of shipping modes, but the standard class is by far the most popular.
- The store has branches all over the country and the sales distribution is well spread out. The West brings in the most though, at 31.6% followed by East, Central and South.
- Copiers are the most profitable sub-category of products, whereas tables are the least profitable, actually producing a net loss.
- Continuing with sub-categories, Phones bring in the most revenue at 14.4% followed closely by Chairs at 14.3%