This project performs retail sales analysis using MySQL. It helps in understanding customer behavior, spending habits, product trends, and seasonal sales variations. The database vanshu_sales stores transaction data, and SQL queries are used to extract meaningful business insights.
The project contains a single table retail_sale, which holds transactional data.
| Column Name | Data Type | Description |
|---|---|---|
Transaction_ID |
BIGINT NOT NULL PRIMARY KEY |
Unique transaction identifier |
Dates |
DATE |
Date of the transaction |
Customer_ID |
VARCHAR(10) |
Unique customer identifier |
Gender |
VARCHAR(10) |
Customer's gender (Male/Female) |
Age |
INT |
Customer's age |
Product_Category |
VARCHAR(10) |
Product category purchased |
Quantity |
INT |
Number of items bought |
Price_per_Unit |
INT |
Price per unit of the product |
Total_Amount |
INT |
Total transaction amount (Quantity * Price_per_Unit) |
- ✅ Check for NULL values to ensure data completeness.
- ✅ Count total transactions recorded in the dataset.
- 💰 Total revenue generated from all sales.
- 📊 Average transaction amount spent per purchase.
- 🏷️ Unique customers count to measure customer base.
- 🏆 Top spending customers by total purchases and total amount spent.
- 📅 Daily, Weekly, Monthly, and Yearly sales trends to identify patterns.
- 🔥 Highest sales revenue date to find peak transaction days.
- 📈 Sales trends by season (Winter, Spring, Monsoon, Autumn).
- 🕒 Peak sales hours or days of the week to determine when customers buy most.
- 💸 Spending habits by gender (average spending per purchase).
- 🎂 Average age of purchasing customers (overall & by gender).
- 🛍️ Most frequently purchased product category.
- 📦 Average price per unit for different product categories.
- 📊 Highest average quantity per transaction by product category.
- 🏷️ Most frequently purchased product category.
- Clone this repository to your local machine:
git clone https://github.com/your-username/vanshu-sales-analysis.git