Supermarket analysis

Introduction
For the months of January, February and March 2019, a supermarket data set has been made available to see how the supermarket is blooming. Mrs. Ruby, the chief growth officer would delight to see the supermarket analysis generate insights. Elizabeth was hired by Mrs. Ruby to work as a data analyst to analyze, visualize, and produce insights.

Derived data will consist of ;

- Total quality
- Total unit price
- Top 5 product line by the total
- Total by branch
- Quality by product line and month
- Gross Margin percentage by payment

Data Preparation
The data came in CSV format and formatted as shown in the diagram below;

The column headers were made to stand out in order to make the data appear more appealing.

Data Analysis
I had to start by identifying the main performance measures, which were the total quality and the unit price. The diagram below demonstrates how I was able to identify these KPIs using DAX and PowerBI's card visualization.

• Total quality: The SUM function was used to get the total sum of the total quality

• Total unit price: The SUM function was also used here to get the sum of the unit price

• Top five (5) product line by total:

According to the data above, consumers were more inclined to buy these top five products overall, with food and beverages having the highest overall preference and health and beauty having the lowest overall preference.

• Quality by product and month

The relationship between product quality and month is seen in the graph above. According to this graph, January had the largest sales of sports-related products and goods with a value above.

Total by branch

• Gross margin percentage by payment

Following the gross margin by payment, eWallet has the greatest rate, with credit cards having the lowest. The eWallet's popularity as a method of payment may be due to how convenient it is to use when traveling or it is convenient to use while on the go

DASHBOARD

You may access the project dataset file here.

Thanks for reading.