Preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter02/CVD.ipynb.

Loading the libraries

To run this example, you need to install the following libraries:

• mldatasets to load the dataset
• pandas and numpy to manipulate it
• statsmodels to fit the logistic regression model
• sklearn (scikit-learn) to split the data
• matplotlib to visualize the interpretations

You should load all of them first:

Import math
import mldatasets
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

Understanding and preparing the data

The data to be used in this example should then be loaded into a DataFrame we call cvd_df:

cvd_df = mldatasets.load("cardiovascular-disease")

From this, you should be getting 70,000 records and 12 columns. We can take a peek...