https://github.com/sanveed-adnan/supermarket-sales-sql-project

SQL-based data analysis project on supermarket sales performance using SQLite and Power BI.
https://github.com/sanveed-adnan/supermarket-sales-sql-project

business-intelligence data-analysis data-science data-science-projects data-visualization power-bi sales-data sql sqlite

Last synced: 6 days ago
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SQL-based data analysis project on supermarket sales performance using SQLite and Power BI.

• Host: GitHub
• URL: https://github.com/sanveed-adnan/supermarket-sales-sql-project
• Owner: Sanveed-Adnan
• Created: 2024-12-20T21:25:32.000Z (13 days ago)
• Default Branch: main
• Last Pushed: 2024-12-20T21:39:36.000Z (13 days ago)
• Last Synced: 2024-12-24T02:20:12.908Z (10 days ago)
• Topics: business-intelligence, data-analysis, data-science, data-science-projects, data-visualization, power-bi, sales-data, sql, sqlite
• Homepage:
• Size: 137 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Supermarket Sales SQL Project  

## Overview  

This project analyzes supermarket sales data using SQL queries to uncover key business insights such as revenue trends, customer behavior, product performance, and time-based analysis. The results are presented in an interactive Power BI dashboard.

---

## Project Objectives  

- Analyze supermarket sales trends and customer behavior.  

- Identify top-selling products and peak sales periods.  

- Provide actionable business recommendations.  

---

## Tools & Technologies  

- **Database:** SQLite  

- **Data Source:** Supermarket Sales CSV File  

- **Query Language:** SQL  

- **Visualization:** Power BI  

---

## Key Insights Extracted  

### **1. Sales Performance**  

- **Top-Performing Cities:** Cities with the highest total sales.  

- **Product Insights:** Best-selling products and product lines.  

- **Revenue by Payment Method:** Most frequently used payment methods.

### **2. Customer Insights**  

- **Customer Types:** Member vs. Non-Member purchasing behavior.  

- **Spending by Gender:** Comparison of average spending by male vs. female customers.  

### **3. Time-Based Insights**  

- **Monthly Sales Trends:** Revenue over time.  

- **Peak Hours:** Hours with the most sales activity.  

---

## Folder Structure  

- **`supermarket_sales_analysis.sql`** - All SQL Queries  

- **`Cleaned_Sales_Report.csv`** - Exported Data from SQLite  

- **`Supermarket_Sales_Dashboard.pbix`** - Power BI Dashboard File  

---

## How to Run the Project  

1. Clone this repository to your local machine.  

2. Open `SupermarketSales.db` in SQLite.  

3. Execute SQL queries from `supermarket_sales_analysis.sql`.  

4. (Optional) Load `Cleaned_Sales_Report.csv` into Power BI for visualization.

---

## Business Recommendations  

1. Focus marketing efforts on the top-performing cities and best-selling products.  

2. Increase sales promotions during peak sales hours.  

3. Offer loyalty programs targeting member and non-member customers.