Python Feature Engineering Cookbook

How to do it...

To complete this recipe, we need to import the necessary tools and load the data:

1. Let's import pandas, the train_test_split function from scikit-learn, and the EndTailImputer function from Feature-engine:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom feature_engine.missing_data_imputers import EndTailImputer

2. Let's load the dataset:

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

The values at the end of the distribution should be calculated from the variables in the train set.

3. Let's separate the data into train and test sets:

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 of ...