Hands-On Unsupervised Learning with Python

Supervised hello world!

In this example, we want to show how to perform a simple linear regression with bidimensional data. In particular, let's assume that we have a custom dataset containing 100 samples, as follows:

import numpy as npimport pandas as pdT = np.expand_dims(np.linspace(0.0, 10.0, num=100), axis=1)X = (T * np.random.uniform(1.0, 1.5, size=(100, 1))) + np.random.normal(0.0, 3.5, size=(100, 1))df = pd.DataFrame(np.concatenate([T, X], axis=1), columns=['t', 'x'])

We have also created a pandas DataFrame because it's easier to create plots using the seaborn library (https://seaborn.pydata.org). In the book, the code for the plots (using Matplotlib or seaborn) is normally omitted, but it's always present in the repository.

We want ...