Feature Scaling Implementation
Data Preparation & Feature Engineering DS practice problem on Onlearn.
Difficulty: easy.
Topics: Understanding Feature Scaling Implementation, Z-score Standardization, Min-Max Normalization, Broadcasting, Floating Point Precision, Column-wise Aggregation, Data Preprocessing, Linear Algebra, Statistical Analysis, Numerical Computing, Software Engineering, Feature Transformation, Vector Operations, Descriptive Statistics, Array Manipulation, Algorithm Implementation.
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.