import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
# Sample data
data = {
 'Feature1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
 'Feature2': [5, 10, 15, 20, 25, 30, 35, 40, 45, 50],
 'Target': [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
}
df = pd.DataFrame(data)
# Split data into features and target
X = df[['Feature1', 'Feature2']]
y = df['Target']
# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Initialize and fit model
tree = DecisionTreeClassifier(max_depth=3, random_state=42)
tree.fit(X_train, y_train)
# Make predictions
y_pred = tree.predict(X_test)
# Evaluate model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
print('Confusion Matrix:')
print(conf_matrix)
print('Classification Report:')
print(class_report)