Data Cleaning Project – Restaurant Orders Dataset
Project Overview
This project focuses on cleaning a messy restaurant dataset containing missing values, inconsistent formatting, duplicate records, and inconsistent date formats. The goal was to transform the dataset into a clean and reliable format suitable for accurate analysis.
Key Skills Demonstrated
Data cleaning and preprocessing
Handling missing values using logical functions
Removing duplicates using business logic (not just IDs)
Standardizing categorical data (text formatting)
Cleaning and formatting date fields
Creating calculated columns (Total Revenue)
Key Outcome
The dataset was transformed into a clean, consistent format, making it ready for analysis. These improvements ensure accurate reporting, prevent duplicate counting, and support reliable business insights.
What I Would Do Next
If this dataset were used in a business setting, the next steps would include:
Analyzing total revenue by product
Identifying top-performing items
Creating dashboards for sales trends
Supporting business decisions with data insights