Before you model anything, you need clean, reliable data. Data cleaning is the first and often most time-consuming step in any data science workflow.
Common Data Issues
- Missing values and incorrect formats
- Duplicates and outliers
- Inconsistent labels and encodings
- Time zone or unit mismatches
Cleaning Techniques
Use libraries like pandas and numpy to clean and preprocess data efficiently. Always visualize before and after cleaning to confirm changes.
Why It's Critical
Garbage in, garbage out — no model can fix fundamentally flawed data. Investing in proper cleaning ensures better outcomes and trustworthy insights.