The main aim of the project is to dive into the Amazon Sales Data and draw insights from it and analayze which factors affect the sales of different cities and their corrosponding branches using SQL. The project aims to uncover insights into sales trends, customer behavior, and product performance. By leveraging the power of SQL queries, we extract meaningful statistics and patterns from complex datasets.
The dataset consist sales record for three cities of Myanmar which are Naypyitaw, Yangon and Mandalay and their respective branches A, B & C. The sales took place in the first quarter of year 2019. The dataset consist of 1000 records and 17 fields like (Invoice Id, Branch, City, Customer Type, Gender, Product Line, Unit Price, Quantity, VAT, Total, Date, Time, Payment Method, Cogs, Gross Margin Percentage, Gross Income, Rating).
- 1] Data Wrangling
- 2] Feature Engineering
- 3] Exploratory Data Analysis
- 1]Sales Analysis
- 2]Product Analysis
- 3]Customer Analysis
- What is the count of distinct product lines in dataset?
- Which payment method occours most frequently?
- Which product line has highest sales?
- How much revenue is generated each month?
- Which product line generated highest revenue?
- Which city has highest revenue record?
- Which product line incurred highest VAT?
- Which customer type occours most frequently?
- Which branch exceeded average number of product sold?
- Which product line is most frequently associated with each gender?
- Identifying cusstomer type contributing highest revenue?
- Determining day of the week with highest customer rating for each branch?
And many more....