I came across some issues in Chapter 01. Errors occurred when I ran the code.

[marcelotiziano]

When I ran the code:
from matplotlib.colors import ListedColormap

Thanks to Sebastian Raschka for 'plot_decision_regions' function

def plot_decision_regions(X, y, classifier, resolution=0.02):

# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])

# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                     np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())

# plot class samples
for idx, cl in enumerate(np.unique(y)):
    plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                alpha=0.8, c=cmap(idx),
                marker=markers[idx], label=cl)

from sklearn.linear_model import perceptron

from sklearn.datasets import make_classification
X, y = make_classification(30, 2, 2, 0, weights=[.3, .3], random_state=300)

plt.scatter(X[:,0], X[:,1], s=50)

pct = perceptron.Perceptron(max_iter=100, verbose=0, random_state=300, fit_intercept=True, eta0=0.002)
pct.fit(X, y)

plot_decision_regions(X, y, classifier=pct)
plt.title('Perceptron')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

The following error occurred.:
ImportError Traceback (most recent call last)
Cell In[17], line 28
23 for idx, cl in enumerate(np.unique(y)):
24 plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
25 alpha=0.8, c=cmap(idx),
26 marker=markers[idx], label=cl)
---> 28 from sklearn.linear_model import perceptron
30 from sklearn.datasets import make_classification
31 X, y = make_classification(30, 2, 2, 0, weights=[.3, .3], random_state=300)

ImportError: cannot import name 'perceptron' from 'sklearn.linear_model'

Then, I update the line "from sklearn.linear_model import perceptron"
to "from sklearn.linear_model import Perceptron"

And the line "X, y = make_classification(30, 2, 2, 0, weights=[.3, .3], random_state=300)"
to "# Generate synthetic dataset
X, y = make_classification(
n_samples=30, # Total number of samples
n_features=2, # Number of features
n_informative=2, # Number of informative features
n_redundant=0, # Number of redundant features
weights=[0.3, 0.7], # Class weights
random_state=300 # Random seed for reproducibility
)"

After fixing these issues, the code worked properly.