Applying automated regression modeling to the fish market dataset

This section demonstrates how to apply machine learning automation with TPOT to a regression dataset. The section uses the fish market dataset (https://www.kaggle.com/aungpyaeap/fish-market) for exploration and regression modeling. The goal is to predict the weight of a fish. You will learn how to load the dataset, visualize it, adequately prepare it, and how to find the best machine learning pipeline with TPOT:

1. The first thing to do is to load in the required libraries and load in the dataset. With regards to the libraries, you'll need numpy, pandas, matplotlib, and seaborn. Additionally, the rcParams module is imported with matplotlib to tweak the plot stylings a bit. You can find the code for this step in the following block:

   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...