Applying automated classification models to the iris dataset

Let's start simple, with one of the most basic datasets out there – the Iris dataset (https://en.wikipedia.org/wiki/Iris_flower_data_set). The challenge here won't be to build an automated model but to build a model that can outperform the baseline model. The Iris dataset is so simple that even the most basic classification algorithm can achieve high accuracy.

Because of that, you should focus on getting the classification basics down in this section. You'll have enough time to worry about performance later:

1. As with the regression section, the first thing you should do is import the required libraries and load the dataset. You'll need numpy, pandas, matplotlib, and seaborn for starters. The matplotlib.rcParams module is imported to tweak the default stylings.

   Here's the code snippet for library imports and dataset loading:

   import numpy as np
   import pandas as pd
   import matplotlib.pyplot...