📌 Project Overview
This project analyzes a mobile sales dataset (SalesMobile) using PostgreSQL. The goal is to perform data cleaning, business analysis, and generate actionable insights that can help stakeholders in decision-making.
🛠️ Skills & Tools Used
SQL (PostgreSQL)
Aggregate Functions (SUM, AVG, MAX, MIN)
GROUP BY, HAVING, CASE Statements
Window Functions (ROW_NUMBER)
Data Cleaning (Null handling, Duplicate removal, Data type conversions)
✅ Key Business Questions Solved
Which brand has the highest sales (by units & revenue)?
Which month records the highest and lowest sales?
Which city generates the most sales?
Which payment method is most preferred and generates the highest revenue?
What is the average customer age by brand?
On which day of the week are maximum sales recorded?
Which mobile model has the highest demand?
Which customers made the most purchases?
Which brand has the highest customer ratings?
In which year was the maximum revenue generated?
Which age group buys the most by brand?
Who are the top 5 customers by revenue?
For each brand, which city generated the highest sales?
Which brand shows the most consistent sales across months?
And more… (20+ questions solved)
📈 Sample Insights
📌 Samsung generated the highest overall revenue.
📌 Online Payment was the most preferred payment method.
📌 Customers aged 20–30 were the most frequent buyers.
📌 Certain brands showed consistent sales across all months, while others were seasonal.