Applying automated regression modeling to the insurance dataset

This section demonstrates how to apply an automated machine learning solution to a slightly more complicated dataset. You will use the medical insurance cost dataset (https://www.kaggle.com/mirichoi0218/insurance) to predict how much insurance will cost based on a couple of predictor variables. You will learn how to load the dataset, perform exploratory data analysis, how to prepare it, and how to find the best machine learning pipeline with TPOT:

1. As with the previous example, the first step is to load in the libraries and the dataset. We'll need numpy, pandas, matplotlib, and seaborn to start with the analysis. Here's how to import the libraries and load the dataset:

   import numpy as np
   import pandas as pd
   import matplotlib.pyplot as plt
   import seaborn as sns
   from matplotlib import rcParams
   rcParams['axes.spines.top'] = False
   rcParams['axes.spines.right'] = False
   df = pd.read_csv(...