from catboost import CatBoostRegressor from sklearn.datasets import load_boston # Load Boston housing data data = load_boston() # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) # Train the model model = CatBoostRegressor(iterations=1000, learning_rate=0.05, depth=6) model.fit(X_train, y_train) # Predict on the test set y_pred = model.predict(X_test)In this code, we first load the Boston housing dataset using the `load_boston` function from Scikit-learn. Next, we split the data into training and testing sets using the `train_test_split` function. We then create an instance of the `CatBoostRegressor` class and specify some hyperparameters such as the number of iterations, learning rate, and tree depth. Finally, we train the model on the training set and predict on the testing set. Overall, the CatBoostRegressor is a powerful tool for regression problems and can often outperform traditional machine learning algorithms. It is available as a package library in Python and can be installed using the `pip` package manager.