Superstore Sales Analysis (SQL + Python)

This project analyzes Global Superstore retail data using SQL (PostgreSQL) and Python.

🚀 Tools: PostgreSQL · SQL · Python · Pandas · Seaborn · VS Code

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📁 Project Structure

• data/ → Contains the raw CSV file
• scripts/ → Python & SQL files
  • load_to_db.py → Loads data into PostgreSQL
  • data_cleaning.sql → Cleans the dataset
  • business_insights.sql → Insight-generating SQL
  • visualization1.py → Generates charts
• visuals/ → Output images and charts
• notebooks/ → Optional Jupyter notebooks
• cleaning_log.md → Cleaning steps documentation
• requirements.txt → Python dependencies
• README.md → Project documentation

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📊 Project Objectives

• Clean raw sales data using SQL
• Extract actionable insights through SQL queries
• Visualize insights using Excel
• Summarize findings with business relevance

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🧹 Data Cleaning Summary

• Verified nulls, standardized casing in columns
• Checked for duplicate orders and invalid dates
• Cleaned discount and profit anomalies
• Logged all cleaning steps in cleaning_log.md

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💼 Key Business Insights & Visuals

1. Are certian product categories are more profitable in specific regions?

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2. Which customers frequently buy from more than one category?

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3. 🔁 First-Time vs Repeat Customers (Profit Comparison)

Do repeat customers bring higher profit?

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4. ⚠️ Cities with High Discounts but Low Profits

Where are we giving too much discount but getting low returns?

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5. 🚚 Profit vs Sales by Shipping Mode

How does shipping mode affect order performance?

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🔍 Summary of Insights

Business Area	Insight Example
Regional Performance	Central & West regions are leading in profit
Customer Behavior	Repeat customers guves higher avg. profit
Product Strategy	Some high-selling products have less profitability
Discount Optimization	Certain cities have high discounts but low returns
Logistics & Delivery	Standard shipping is most used, but not always most profitable

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📌 How to Run the Project

1. Clone the repo
   git clone https://github.com/darshanr-c/sql_superstore_analysis.git

2. Activate the virtual environment
   source .venv/bin/activate

3. Install Python dependencies
   pip install -r requirements.txt

4. Load data into PostgreSQL
   python scripts/load_to_db.py

5. Run cleaning & analysis

   • SQL: scripts/data_cleaning.sql & business_insights.sql
   • Python: scripts/visualization1.py

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🧰 Environment

• PostgreSQL 14+ installed locally
• SQL queries run using:
  • VS Code + PostgreSQL extension (recommended)
  • Or DBeaver / psql CLI
• Python 3.9+ with the following libraries:
  • pandas, sqlalchemy, psycopg2-binary, seaborn, matplotlib

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Author

Darshan Chaudhari
Master’s in Data Science · Germany
LinkedIn →

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