How do I choose the right preprocessing techniques for my data?

How do I choose the right preprocessing techniques for my data?

Choosing the right preprocessing techniques depends on your data type and the analysis goals. What kind of data are you working with?

I have a dataset with both numerical and categorical features.

For numerical features, consider normalization or standardization. For categorical features, you might use one-hot encoding or label encoding. Do you need help with specific techniques?

Yes, can you explain when to use normalization versus standardization?

Normalization is useful when you want to scale data to a range, typically [0, 1]. Standardization is better when your data follows a Gaussian distribution. It centers the data around zero with a standard deviation of one.

Got it! What about handling missing values?

You can handle missing values by imputation, where you fill in missing data with mean, median, or mode, or by removing rows or columns with missing values. It depends on the extent of missingness and your analysis needs.