I performed an analysis on the Supermarket Sales dataset to identify the purchasing behaviors of customers. This involved defining relevant metrics, such as total sales and drivers of sales, as well as determining the sources of the highest sales. I thoroughly explored the data and created charts and graphs to visualize any interesting trends and insights discovered.
Dataset info Invoice id: Computer generated sales slip invoice identification number
Branch: Branch of supercenter (3 branches are available identified by A, B and C).
City: Location of supercenters
Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card.
Gender: Gender type of customer
Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel
Unit price: Price of each product in $
Quantity: Number of products purchased by customer
Tax: 5% tax fee for customer buying
Total: Total price including tax
Date: Date of purchase (Record available from January 2019 to March 2019)
Time: Purchase time (10am to 9pm)
Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet)
COGS: Cost of goods sold
Gross margin percentage: Gross margin percentage
Gross income: Gross income
Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)
Here are some results of EDA:
- Average Sales by Product Line and City
2.Total Sales by Product Line and Gender
- Distribution of Profit by Product Line and Customer Type
