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import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier, plot_tree from sklearn.metrics import accuracy_score # Load dataset data = load_breast_cancer() X, y = data.data, data.target # Train-test split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train Decision Tree clf = DecisionTreeClassifier(random_state=42) clf.fit(X_train, y_train) # Accuracy y_pred = clf.predict(X_test) print(f"Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%") # Predict one sample sample = X_test[0] predicted_label = clf.predict([sample])[0] print("Predicted Class:", "Benign" if predicted_label == 1 else "Malignant") # Plot tree plt.figure(figsize=(12, 8)) plot_tree(clf, filled=True, feature_names=data.feature_names, class_names=data.target_names) plt.title("Decision Tree - Breast Cancer") plt.show()

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