Retail Sales Data Analytics Pipeline

This project analyzes retail sales data using Python, SQL, and SQLite.

Technologies Used

• Python
• SQL
• SQLite
• Pandas
• Matplotlib
• Seaborn

Data Pipeline

This project implements a simple data pipeline:

1. Extract – Load retail sales CSV dataset
2. Transform – Clean column names, remove duplicates, handle missing values
3. Load – Store processed data in SQLite database
4. Analyze – Execute SQL queries to extract insights
5. Visualize – Generate charts using Python

Insights Generated

• Revenue by product category
• Regional sales performance
• Top performing product sub-categories

Charts

Revenue by Category

Revenue by Region

Top Sub-Categories by Revenue

How to Run

Install dependencies

pip install pandas matplotlib seaborn

Load dataset

python load_data.py

Run analysis

python run_analysis.py