Embedding feature creation in a scikit-learn pipeline

Throughout this chapter, we discussed how to automatically create and select features from time series data by utilizing tsfresh. Then, we used these features to train a classification model to predict whether an office was occupied at any given hour.

The tsfresh library offers wrapper classes around its main functions, extract_features and extract_relevant_features, to make features that have been created from time series compatible with the scikit-learn pipeline.

In this recipe, we will line up the process of creating features with tsfresh and training a logistic regression model in a scikit-learn pipeline.

How to do it...

Let’s begin by importing the necessary libraries and getting the dataset ready:

1. Let’s import the required libraries and functions:

   import pandas as pd
   from sklearn.pipeline import Pipeline
   from sklearn.linear_model import LogisticRegression
   from sklearn.model_selection import...