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%