Applying automated regression modeling to the vehicle dataset

This section shows how to develop an automated machine learning model on the most complex dataset thus far. You will use the vehicle dataset (https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho), so download it if you haven't already. The goal is to predict the selling price based on the various predictors, such as year made and kilometers driven.

This time, we won't focus on exploratory data analysis. You can do that on your own if you've followed the last two examples. Instead, we'll concentrate on dataset preparation and model training. There's a lot of work required to transform this dataset into something ready for machine learning, so let's get started right away:

1. Once again, the first step is to load in the libraries and the dataset. The requirements are the same as with previous examples. You'll need numpy, pandas, matplotlib, and seaborn. Here's how...