The objective of this ETL project is to perform comprehensive sales analysis by extracting sales data from Kaggle, processing it using Python (Pandas) to handle missing values, correct formatting issues, and transform raw data into a structured format, and then loading it into a MySQL database for further analysis. The goal is to gain insights into key business metrics such as sales trends, product performance, regional sales distribution, and customer segment analysis. By analyzing the data, the project aims to identify high-performing product categories, track month-over-month and year-over-year sales growth, monitor profitability across regions, and forecast potential stockouts for top-selling products. This analysis helps inform data-driven business decisions, optimize inventory management, and guide marketing strategies.
- Extracted raw data from Kaggle, performed data cleaning & transformation using Python (Pandas, NumPy), and stored the cleaned data in CSV format.
- Loaded the cleaned dataset into MySQL, ensuring data integrity and optimizing table structures for efficient querying.
- Developed business-critical SQL queries to analyze key insights such as purchase frequency, sales trends, and profitability across different regions and product categories.
- Implemented advanced SQL techniques including window functions (DENSE_RANK, LAG), conditional aggregations (CASE), and CTEs to answer complex business questions.
- Conducted time-series analysis, calculating Month-over-Month (MoM) and Year-over-Year (YoY) sales growth to identify sales performance trends.
- Identified top-selling & most profitable products per region to support inventory management and prevent stockouts.