This project analyzes an E-Commerce dataset analysis using SQL queries and Python for insights & visualizations.
It explores customer behavior, sales patterns, and business performance by answering both basic and advanced analytical questions.
The dataset includes information about:
- Customers – Demographic details (city, state, ID mapping)
- Orders – Order IDs, timestamps, status
- Order Items – Product ID, seller ID, quantity, price, freight value
- Products – Category, dimensions, weight
- Payments – Payment type, installments, value
- Sellers – Seller details and performance
- Geolocation – City, state, latitude, longitude
Using these tables, SQL was used to extract insights, and Python (Pandas, NumPy, Matplotlib, Seaborn) was used for further analysis.
- Unique customer cities
- Orders placed in 2017
- Sales per product category
- % of orders with installments
- Customers count per state
- Orders per month in 2018
- Avg. products per order (by city)
- % revenue by category
- Correlation: product price vs. purchase frequency
- Revenue per seller (ranked)
- Moving average of order values per customer
- Cumulative sales per month (by year)
- Year-over-year sales growth rate
- Customer retention (within 6 months)
- Top 3 spenders per year
- SQL – Data extraction, transformations, aggregation
- Python – Data cleaning, analysis & visualization
- Pandas, NumPy, Matplotlib, Seaborn
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Clone this repository:
git clone https://github.com/your-username/ecommerce-data-analysis.git cd ecommerce-data-analysis -
Import dataset files into your SQL database (MySQL/PostgreSQL/SQLite).
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Run queries from Questions.txt or queries.sql.
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Open Ecommerce_python_sql.ipynb in Jupyter Notebook to view the Python analysis.