Sales Insights with SQL and Python

Analyze a retail sales dataset using SQL (SQLite) for data aggregation and Python for visualization.
This project demonstrates how to move from raw transactional data → structured SQL database → actionable business insights with clear visuals.

Project Overview

This project combines data engineering, SQL analytics, and data visualization to uncover key sales insights from a retail dataset.
You’ll see how SQL handles powerful data aggregation, while Python brings the results to life with visualizations.

Workflow:

1. Load CSV data → SQLite database
2. Run SQL queries for KPIs
3. Export insights to CSV
4. Visualize results with charts

Project Structure

sales-insights-sql/
├─ README.md
├─ requirements.txt
├─ data/
│  └─ sales_data.csv
├─ src/
│  ├─ create_db.py
│  ├─ queries.sql
│  ├─ analyze_sales.py
│  └─ utils.py
└─ outputs/
   ├─ charts/
   │   ├─ revenue_by_region.png
   │   └─ monthly_sales_trend.png
   ├─ revenue_by_region.csv
   ├─ top_products_by_revenue.csv
   ├─ top_products_by_quantity.csv
   ├─ monthly_sales_trend.csv
   └─ aov_summary.csv

Dataset

A synthetic dataset of 10 products sold across 4 regions (North, South, East, West) over one year.

Example preview:

Setup & Usage

Create Virtual Environment

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

pip install -r requirements.txt

Create SQLite Database

python src/create_db.py --csv data/sales_data.csv --db sales.db

Run SQL Analytics + Visualization

python src/analyze_sales.py --db sales.db --sql src/queries.sql --outdir outputs

SQL Queries Summary

Inside src/queries.sql, five core analyses are defined:

Example SQL snippet:

SELECT region, ROUND(SUM(revenue), 2) AS total_revenue
FROM sales
GROUP BY region
ORDER BY total_revenue DESC;

Example Results

Revenue by Region

Insight:

The West and East regions generated the highest revenues, indicating stronger customer engagement and product performance in those markets.

Monthly Sales Trend

Insight:

Sales consistently rise through Q3 and Q4, showing a clear seasonal trend — possibly due to holiday promotions or end-of-year demand spikes.

Top Products by Revenue

Laptops and Smartphones dominate revenue, accounting for over 50% of total sales.

Average Order Value (AOV)

Higher AOV in the West indicates customers purchase more premium or bulk items.

Tools & Libraries

Key Learnings

• Combine SQL and Python for real-world analytics workflows
• Build reproducible, automated reports
• Apply common business KPIs: revenue, trends, and AOV
• Communicate insights effectively with visuals

Example Insight Summary

• The West region leads in total revenue.
• Laptops are the most profitable product category.
• Revenue increases significantly toward Q4, suggesting seasonal buying patterns.
• Average order values differ by region, hinting at regional purchasing behavior differences.

Conclusion

Sales Insights with SQL and Python is a compact yet powerful example of end-to-end data analytics.
It highlights your ability to:

• Work with databases and SQL
• Automate reporting pipelines
• Produce professional visuals
• Communicate meaningful business insights