How do I encode categorical features using scikit-learn?

In order to include categorical features in your Machine Learning model, you have to encode them numerically using "dummy" or "one-hot" encoding. But how do you do this correctly using scikit-learn? In this video, you'll learn how to use OneHotEncoder and ColumnTransformer to encode your categorical features and prepare your feature matrix in a single step. You'll also learn how to include this step within a Pipeline so that you can cross-validate your model and preprocessing steps simultaneously. Finally, you'll learn why you should use scikit-learn (rather than pandas) for preprocessing your dataset. AGENDA: 0:00 Introduction 0:22 Why should you use a Pipeline? 2:30 Preview of the lesson 3:35 Loading and preparing a dataset 6:11 Cross-validating a simple model 10:00 Encoding categorical features with OneHotEncoder 15:01 Selecting columns for preprocessing with ColumnTransformer 19:00 Creating a two-step Pipeline 19:54 Cross-validating a Pipeline 21:44 Making predictions on new data 23:43 Recap of the lesson 24:50 Why should you use scikit-learn (rather than pandas) for preprocessing? CODE FROM THIS VIDEO: https://github.com/justmarkham/scikit-learn-videos/blob/master/10_categorical_features.ipynb WANT TO JOIN MY NEXT LIVE WEBCAST? Become a member ($5/month): https://www.patreon.com/dataschool === RELATED RESOURCES === OneHotEncoder documentation: https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-categorical-features ColumnTransformer documentation: https://scikit-learn.org/stable/modules/compose.html#columntransformer-for-heterogeneous-data Pipeline documentation: https://scikit-learn.org/stable/modules/compose.html#pipeline My video on cross-validation: https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7 My video on grid search: https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8 My lesson notebook on StandardScaler: https://nbviewer.jupyter.org/github/j