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This project focuses on analyzing a jewelry e-commerce dataset to extract meaningful insights about user purchases, product categories, and sales performance.
Python (Pandas) is used for data cleaning and preprocessing, while SQL queries handle complex data retrieval, aggregation, joining, and optimization.

• Cleaned and preprocessed raw sales data with Python to prepare it for database import
• Designed and created relational database tables (users,products, andjewelry_sales)
• Performed advanced SQL queries including filtering, grouping, aggregation, and multiple types of JOINs
• Used subqueries and created SQL views to simplify and speed up analysis
• Optimized query performance by adding indexes on key columns
• Extracted key business insights such as total revenue, popular categories, and material-wise sales

The dataset was cleaned using Pandas to ensure consistent column names and prepare for further SQL analysis.

importpandasaspddf=pd.read_csv("jewelry.csv")df.columns= ["event_time","user_id","item_id","quantity","product_id","category","is_purchase","price","session_id","unknown_flag","color","material","gem"]df.to_csv("jewelry_cleaned.csv",index=False)

Python: Data cleaning and CSV manipulation using PandasSQL: Complex query writing including JOINs, subqueries, grouping, aggregationDatabase Design: Creating normalized tables and views for modular queryingQuery Optimization: Using indexes to speed up database performanceData Analysis: Extracting actionable business insights from sales dataData Visualization: (Optional) Plotting charts with Matplotlib if applicable