Python Feature Engineering Cookbook

How to do it...

To begin, let's make a few imports and prepare the data:

1. Let's import pandas and the required functions and classes from scikit-learn and Feature-engine:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.impute import SimpleImputerfrom feature_engine.missing_data_imputers import FrequentCategoryImputer

2. Let's load the dataset:

data = pd.read_csv('creditApprovalUCI.csv')

3. Frequent categories should be calculated using the train set variables, so let's separate the data into train and test sets and their respective targets:

X_train, X_test, y_train, y_test = train_test_split(    data.drop('A16', axis=1), data['A16'], test_size=0.3,     random_state=0)

Remember that you can check the percentage ...