This project focuses on cleaning and validating a customer dataset.
The goal is to build a reliable cleaning workflow and prepare the data for analysis.
Review the dataset and check for:
- Missing values
- Duplicate records
- Invalid values
- Incorrect data types
- Inconsistent categories
- Suspicious numerical values
- Outliers
- Internal inconsistencies between related columns
Do not automatically delete or replace suspicious values.
Each cleaning decision should be based on the meaning of the data.
Raw Data
→ Initial Inspection
→ Data Cleaning
→ Validation
→ Clean Dataset
→ Documentation
Your repository should include:
- Cleaned dataset
- README.md
- Notebook / Script / Excel / Power Query file
Before submission, make sure:
- The workflow runs correctly
- Important cleaning decisions are documented
- The raw data is preserved
- The final dataset is validated
- File and folder names are clear
Submit your project through GitHub.
The main focus of this project is not the number of changes you make, but the quality of your decisions.
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