Implementing a data pipeline in Python
Now that we’ve discussed the importance of consistency in feature engineering across training and inference, it’s time to dive into the practical side of building data pipelines. In this chapter, we will focus on implementing feature engineering pipelines in Python that will allow you to automate the process, making it repeatable and reliable for both time series forecasting and regression tasks. This reduces the risk of data leakage and ensures that your model performs optimally when it’s deployed in production. Let’s explore how to implement these pipelines using Python’s scikit-learn and XGBoost libraries.
Time series feature engineering in a pipeline
As you learned in Chapter 9, time series data poses unique challenges due to the temporal dependencies between observations. In this section, you will perform feature engineering for time series data and use a pipeline to combine the features with model...
