A simple machine learning example using Python and the scikit-learn library for the classification of the Iris dataset. The Iris dataset is a classic dataset containing measurements of iris flowers and their species. We will use a Decision Tree classifier to classify the species based on the measurements.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split the dataset into train and test sets (70% train, 30% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create a Decision Tree classifier and train it on the train set
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

# Make predictions on the test set
y_pred = clf.predict(X_test)

# Calculate the accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy of the Decision Tree classifier: {accuracy:.2f}")

This code will output the accuracy of the Decision Tree classifier on the Iris dataset. We can experiment with other classifiers and their parameters to see how the results change.