Retail Sales Performance Analysis

SQL + Python Data Analyst Project

Project Overview

A complete end-to-end Data Analysis project using SQL and Python to analyze retail sales data and generate business insights.

Tools and Technologies Used

• Python 3
• Google Colab
• SQLite / MySQL
• Pandas
• Matplotlib
• Seaborn
• MySQL Workbench

Dataset

Three related tables created synthetically:

• customers — 20 rows
• products — 10 rows
• sales — 300 rows

Project Structure

Retail-Sales-SQL-Python-Project/

├── retail_sales.db

├── retail_sales_mysql.sql

├── Retail_Sales_Report.xlsx

├── notebooks/

│ └── Retail_Sales_Project.ipynb

├── charts/

│ ├── 01_monthly_revenue.png

│ ├── 02_category_bar.png

│ ├── 03_top_customers.png

│ ├── 04_pie_chart.png

│ ├── 05_scatter.png

│ └── 06_heatmap.png

└── README.md

Key Business Insights

• Electronics drives the highest revenue
• Top 5 customers contribute 30% of total revenue
• Mumbai and Delhi are the top revenue-generating cities
• Discounts above 15% reduce average order value
• Mid-year (May to July) is the peak sales season

Charts

Monthly Revenue Trend

Revenue by Category

Top 5 Customers

SQL Concepts Covered

• SELECT, WHERE, GROUP BY, ORDER BY
• JOIN (INNER JOIN)
• Aggregate functions (SUM, COUNT, AVG)
• Subqueries
• CTEs (WITH clause)
• Window functions (RANK, SUM OVER)

Python Concepts Covered

• Data generation with NumPy and random
• Data cleaning with Pandas
• Merging multiple DataFrames
• GroupBy aggregations
• Data visualization with Matplotlib and Seaborn
• Exporting to Excel with openpyxl

How to Run

1. Open notebooks/Retail_Sales_Project.ipynb in Google Colab
2. Run all cells from top to bottom
3. All charts save to /content/charts/
4. Excel report saves to /content/Retail_Sales_Report.xlsx

Author

Your Name
[https://www.linkedin.com/in/parshvi-verma-714642190]
[parshviverma@gmai.com]