How can I perform feature engineering using scikit-learn?

How can I perform feature engineering using scikit-learn?

Feature engineering in scikit-learn involves transforming raw data into features that better represent the underlying problem. You can use techniques like scaling, encoding categorical variables, and creating polynomial features.

What are some common techniques for feature engineering?

Common techniques include normalization, one-hot encoding, binning, and creating interaction features. Each technique helps improve model performance.

Can you show me an example of one-hot encoding?

Sure! You can use pd.get_dummies() in pandas to perform one-hot encoding. For example, if you have a DataFrame with a column 'Color' containing 'Red', 'Blue', and 'Green', pd.get_dummies(df, columns=['Color']) will create new binary columns for each color.

How do I handle missing values during feature engineering?

You can handle missing values by using techniques like imputation (filling in missing values with the mean, median, or mode) or by dropping rows/columns with missing values. Scikit-learn provides SimpleImputer for this purpose.