Write a Python function that performs feature scaling on a dataset using both standardization and min-max normalization. The function should take a 2D NumPy array as input, where each row represents a data sample and each column represents a feature. It should return two 2D NumPy arrays: one scaled by standardization and one by min-max normalization. Make sure all results are rounded to the nearest 4th decimal.
Examples
Example 1:
Input:
data = np.array([[1, 2], [3, 4], [5, 6]])Output:
([[-1.2247, -1.2247], [0.0, 0.0], [1.2247, 1.2247]], [[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])Explanation: Standardization rescales the feature to have a mean of 0 and a standard deviation of 1.
Min-max normalization rescales the feature to a range of [0, 1], where the minimum feature value
maps to 0 and the maximum to 1.
Starter Code
def feature_scaling(data: np.ndarray) -> (np.ndarray, np.ndarray):
# Your code here
return standardized_data, normalized_dataPython3
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