I have used Python to clean, shape, and I have also used it to get stastical meaning.Then, I have used PowerBi and Python to visualise the data.
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)
Day: Days of purchase
Month:Month of purchase
Day Number: Each number corresponds to each day.
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)
Power Bi
Average Rating Score of Different Cities between Days
Is there are a relation between total price and ratings?
Which day cities make most sales?
Which day supermarkets make most sales?
Is there a distinctive day that has to be observed because of dramatic decrease in total sales and customer satisfaction(rating)?
Which day is the most effective to make advertisement works for the spesific product?
Is there are a difference between different customer types and total prices?
https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales
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