Customer Data Cleaning (Polars)
Problem
Customer/CRM data often contains errors:
invalid values (e.g. impossible birth years)
extreme outliers (e.g. unrealistic income)
inconsistent categories (e.g. "YOLO", "Absurd")
Such data cannot be reliably used for analysis or business decisions.
Solution
This project implements a data cleaning using Polars.
It:
detects invalid records
separates invalid vs review cases
removes clearly incorrect data
validates results at every step
Cleaning Rules
Birth year < 1900 → removed
Income > 200000 → removed
Marital status "YOLO" / "Absurd" → removed
"Alone" → kept for manual review
Results
Original rows: 2240
Removed rows: 7
Final dataset: 2233
The dataset is now consistent and ready for analysis.
Output
cleaned_customer_data.csvTech Stack
Python
Polars
Use Case
Applicable to:
CRM systems
marketing datasets
customer databases
Key Insight
Data cleaning is not about guessing values.
It is about:
defining clear rules
validating results
ensuring data consistency
0 likes · 4 views · Mar 29, 2026
Implemented data cleaning using Polars to refine customer data.
