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+"""
+CatBoost Regressor Example.
+
+This script demonstrates the usage of the CatBoost Regressor for a simple regression task.
+CatBoost is a powerful gradient boosting library that handles categorical features automatically
+and is highly efficient.
+
+Make sure to install CatBoost using:
+    pip install catboost
+
+Contributed by: @AHuzail
+"""
+
+import numpy as np
+from sklearn.datasets import load_boston
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+from catboost import CatBoostRegressor
+
+
+def data_handling() -> tuple:
+    """
+    Loads and handles the dataset, splitting it into features and targets.
+
+    The Boston dataset is used as a regression example.
+    
+    Returns:
+        tuple: A tuple of (features, target), where both are numpy arrays.
+
+    Example:
+    >>> features, target = data_handling()
+    >>> features.shape
+    (506, 13)
+    >>> target.shape
+    (506,)
+    """
+    # Load Boston dataset (note: this dataset may be deprecated, replace if needed)
+    boston = load_boston()
+    features = boston.data
+    target = boston.target
+    return features, target
+
+
+def catboost_regressor(features: np.ndarray, target: np.ndarray) -> CatBoostRegressor:
+    """
+    Trains a CatBoostRegressor using the provided features and target values.
+
+    Args:
+        features (np.ndarray): The input features for the regression model.
+        target (np.ndarray): The target values for the regression model.
+
+    Returns:
+        CatBoostRegressor: A trained CatBoost regressor model.
+
+    Example:
+    >>> features, target = data_handling()
+    >>> model = catboost_regressor(features, target)
+    >>> isinstance(model, CatBoostRegressor)
+    True
+    """
+    regressor = CatBoostRegressor(iterations=100, learning_rate=0.1, depth=6, verbose=0)
+    regressor.fit(features, target)
+    return regressor
+
+
+def main() -> None:
+    """
+    Main function to run the CatBoost Regressor example.
+
+    It loads the data, splits it into training and testing sets,
+    trains the regressor on the training data, and evaluates its performance
+    on the test data.
+    """
+    # Load and split the dataset
+    features, target = data_handling()
+    x_train, x_test, y_train, y_test = train_test_split(
+        features, target, test_size=0.25, random_state=42
+    )
+
+    # Train CatBoost Regressor
+    regressor = catboost_regressor(x_train, y_train)
+
+    # Predict on the test set
+    predictions = regressor.predict(x_test)
+
+    # Evaluate the performance using Mean Squared Error
+    mse = mean_squared_error(y_test, predictions)
+    print(f"Mean Squared Error on Test Set: {mse:.4f}")
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod(verbose=True)
+    main()