Your task is to train and evaluate two boosting models — AdaBoost and Gradient Boosting — on the Breast Cancer dataset.

Follow these steps:

1. Load the dataset using load_breast_cancer() from sklearn.datasets.
2. Split the data into training and testing sets (test_size=0.3, random_state=42).
3. Train:
   • An AdaBoostClassifier with:
     • base_estimator=DecisionTreeClassifier(max_depth=1)
     • n_estimators=50, learning_rate=0.8
   • A GradientBoostingClassifier with:
     • n_estimators=100, learning_rate=0.1, max_depth=3.
4. Evaluate both models on the test data using accuracy_score.
5. Print both accuracies.