Technical requirements
In this chapter, we will use the Python libraries Matplotlib, pandas, NumPy, scikit-learn, and Feature-engine. If you need to install Python, the free Anaconda Python distribution (https://www.anaconda.com/) includes most numerical computing libraries.
feature-engine can be installed with pip as follows:
pip install feature-engine
If you use Anaconda, you can install feature-engine with conda:
conda install -c conda-forge feature_engine
Note
The recipes from this chapter were created using the latest versions of the Python libraries at the time of publishing. You can check the versions in the requirements.txt file in the accompanying GitHub repository, at https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/requirements.txt.
We will use the Credit Approval dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/), licensed under the CC BY 4.0 creative commons attribution: https://creativecommons.org/licenses/by/4.0/legalcode. You’ll find the dataset at this link: http://archive.ics.uci.edu/dataset/27/credit+approval.
I downloaded and modified the data as shown in this notebook: https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/ch01-missing-data-imputation/credit-approval-dataset.ipynb
We will also use the air passenger dataset located in Facebook’s Prophet GitHub repository (https://github.com/facebook/prophet/blob/main/examples/example_air_passengers.csv), licensed under the MIT license: https://github.com/facebook/prophet/blob/main/LICENSE
I modified the data as shown in this notebook: https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/ch01-missing-data-imputation/air-passengers-dataset.ipynb
You’ll find a copy of the modified data sets in the accompanying GitHub repository: https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/ch01-missing-data-imputation/
