Café Sales Data Cleaning (Python & Pandas Project)

I worked on cleaning and preparing a real-world café sales dataset to make it ready for accurate analysis and visualization.

The dataset contained raw transactional records including items, prices, quantities, payment methods, and transaction dates.

**Data Cleaning Steps:**

* Checked for duplicates and inconsistent values

* Converted columns to proper data types (numeric & datetime)

* Handled missing values logically (using Quantity × Price = Total)

* Removed incomplete or illogical records

* Standardized text columns for consistency

* Exported the cleaned dataset for future dashboard development

**Tools Used:**

Python | Pandas | Jupyter Notebook

This project improved the dataset’s accuracy and reliability — ensuring clean, structured data that can be confidently used for BI dashboards and data analysis.