What are some common data cleaning mistakes to avoid?

What are some common data cleaning mistakes to avoid?

Some common mistakes include not handling missing values properly, failing to standardize formats, and ignoring outliers.

Can you explain why handling missing values is important?

Handling missing values is crucial because they can skew your analysis and lead to incorrect conclusions.

What methods can I use to handle missing data?

You can use methods like imputation, deletion, or using algorithms that support missing values.

How do I identify outliers in my dataset?

You can identify outliers using statistical methods like Z-scores, IQR, or visualizations like box plots.