Sales Data Cleaning & Structuring

This project focused on transforming raw, unstructured sales records into a clean, structured, and analysis-ready dataset suitable for reporting, dashboard creation, and data-driven decision-making.

The process involved cleaning and restructuring the original data by standardizing column names, organizing financial fields, merging date and time into a consistent format, and removing unnecessary columns and non-essential values. The final dataset was prepared to support downstream analysis, business reporting, and visualization workflows.

To ensure confidentiality while showcasing the project, sensitive business data was anonymized by replacing customer names and related commercial information with coded identifiers. This made it possible to present the dataset safely without exposing any real business or customer details.

Overall, this project demonstrates my ability to work with real-world messy data, improve its structure and quality, and convert it into a reliable dataset ready for analysis, reporting, and business insights.