task-1

# Import necessary libraries
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Generate some example data
np.random.seed(0)
X = np.random.rand(100, 2)
y = (X[:, 0] + X[:, 1] > 1).astype(int)  # Binary classification: True if X1 + X2 > 1, else False

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a Decision Tree Classifier
model = DecisionTreeClassifier(random_state=42)

# Train the model on the training data
model.fit(X_train, y_train)

# Make predictions on the test data
y_pred = model.predict(X_test)

# Calculate accuracy and other metrics to evaluate the model
accuracy = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f"Accuracy: {accuracy}")
print("Confusion Matrix:")
print(confusion)
print("Classification Report:")
print(report)