🛍️ Retail Data Analysis Project (Python + SQL Server)

This project demonstrates a full-cycle data analysis pipeline using Python (Jupyter Notebook) and SQL Server Management Studio, centered around a retail dataset. It highlights my technical ability to extract, clean, load, and analyze real-world data using professional tools and best practices.

🔍 Project Overview:

Extracted the retails.csv dataset using the Kaggle API directly into the Python environment for a streamlined data acquisition process.

Performed data cleaning and preprocessing with the Pandas library, including handling missing values, formatting inconsistencies, and preparing the dataset for analysis.

Loaded the cleaned dataset into SQL Server using the Pandas library in combination with the pyodbc connector, enabling a seamless and structured data transfer from the DataFrame to the SQL table.

Created a new SQL table in SQL Server Management Studio to store the cleaned data for further querying and reporting.

Conducted data analysis using T-SQL, uncovering insights into sales trends, discount effectiveness, product categories, and regional performance.

This project reflects my end-to-end skill set across data extraction, transformation, loading (ETL), and analysis, combining Python automation with SQL-based insight generation.