The preparation

You can find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter09/Traffic.ipynb.

Loading the libraries

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

• mldatasets to load the dataset
• pandas and numpy to manipulate the dataset
• tensorflow to load the model
• statsmodels, sklearn (scikit-learn), matplotlib, seaborn, alibi, distython, shap, and SALib to create and visualize the interpretations

You should load all of them first:

import math
import os
import mldatasets
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
from keras.utils.data_utils import get_file
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import...