What are some common data preprocessing mistakes to avoid?

What are some common data preprocessing mistakes to avoid?

Some common mistakes include not handling missing values, ignoring outliers, and not normalizing data.

Can you explain why handling missing values is important?

Handling missing values is crucial because they can lead to biased results and affect the model's performance.

What techniques can I use to handle missing values?

You can use techniques like imputation, removing missing data, or using algorithms that support missing values.

How do I know if my data is normalized?

You can check if your data is normalized by looking at the distribution of your features; they should have similar scales.