generating and visualizing a dataset using Scikit-learn and the make_moons() function

two_moons.py

# 1- Generating a dataset.
from sklearn.datasets import make_moons
 
# X are the generated instances, an array of shape (500,2).
# y are the labels of X, with values of either 0 or 1.
X, y = make_moons(n_samples=500, noise=0.3, random_state=42)

# 2- Visualizing the dataset.
from matplotlib import pyplot as plt

# When the label y is 0, the class is represented with a blue square.
# When the label y is 1, the class is represented with a green triangle.
plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs")
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "g^")

# X contains two features, x1 and x2
plt.xlabel(r"$x_1$", fontsize=20)
plt.ylabel(r"$x_2$", fontsize=20)

# Simplifying the plot by removing the axis scales.
plt.xticks([])
plt.yticks([])

# Displaying the plot.
plt.show()