Python Feature Engineering Cookbook

How to do it...

To complete this recipe, let's import the required libraries and load the data:

1.  Let's import the required Python libraries and classes:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import BayesianRidgefrom sklearn.experimental import enable_iterative_imputerfrom sklearn.impute import IterativeImputer

2. Let's load the dataset with some numerical variables:

variables = ['A2','A3','A8', 'A11', 'A14', 'A15', 'A16']data = pd.read_csv('creditApprovalUCI.csv', usecols=variables)

The models that will be used to estimate missing values should be built on the train data and used to impute values in the train, test, and future data:

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