# Main take-away
## Wrap-up
In this module, you learned:
- to create a scikit-learn predictive model;
- about the scikit-learn API to train and test a predictive model;
- to process numerical data, notably using a `Pipeline`.
- to process categorical data, notably using a `OneHotEncoder` and an
`OrdinalEncoder`;
- to handle and process mixed data types (i.e. numerical and
categorical data), notably using a `ColumnTransformer`.
## To go further
You can refer to the following scikit-learn examples which are related to
the concepts approached during this module:
- [Predictive machine learning pipeline with mixed data types](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py)
- [Importance of feature scaling](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py)