Rafsanjancity-Vendors-MySQL-PowerBI

Data Used

Data - https://www.foodcenter.ir/rafsanjan

**Data Scraping (Listly)

Data Cleaning & Analysis - MySQL(DataGrip)

Data Visualization - PowerBI

Questions

Use SQL or any other suitable tool (e.g., Python, ) to perform data analysis on the dataset. Calculate critical metrics such as customer satisfaction score, and relevant KPIs. Classify the obtained list and label them from A to E.

Summary of Findings

• The number of fast foods is more.
• Average rate of 80 sets 3.3.
• The best-selling ice pack collection with 200 orders and a rate of 4.8.

Limitations

• The challenge in cleaning the first ones was the figure of the number of orders for the collections, which data was not available and only the collection write and the number of comments were recorded, considering that on average, 1 to 5% of the customers of online sales systems act. They register comments, and in this report, I calculated 5% in the number of comments and the amount of collection sales.