Data Cleaning Project: Retail Sales Data (SQL & Python)

📌 Project Overview

A dual-workflow data cleaning project using Python (Pandas) and SQL Server to standardize retail sales data, fix missing values, and validate transactions.

This project demonstrates a comprehensive data cleaning pipeline implemented in two different technologies: Python (Pandas) and SQL. The goal is to take raw, messy retail store sales data, identify inconsistencies, and transform it into a clean, structured format suitable for analysis.

📂 Repository Structure

├── Data/                     # Raw CSV files (retail_store_sales.csv, Product_master.csv)
├── Scripts-python/           # Jupyter Notebooks for Python-based cleaning
│   └── Data-Cleaning.ipynb
├── Scripts-SQL/              # SQL scripts for database-based cleaning
│   ├── Data-import.sql
│   ├── Cleaning-Price-per-unit.sql
│   ├── Item-CLeaning.sql
│   ├── Cleaned-Discount-applied.sql
│   └── Save-Cleaned-Data.sql
└── README.md