Example:
        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]])
        reasoning: 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.

Feature Scaling Techniques

Feature scaling is crucial in many machine learning algorithms that are sensitive to the magnitude of features. This includes algorithms that use distance measures like k-nearest neighbors and gradient descent-based algorithms like linear regression.

Standardization:

Standardization (or Z-score normalization) is the process where the features are rescaled so that they have the properties of a standard normal distribution with a mean of zero and a standard deviation of one: \[ z = \frac{(x - \mu)}{\sigma} \] Where \(x\) is the original feature, \(\mu\) is the mean of that feature, and \(\sigma\) is the standard deviation.

Min-Max Normalization:

Min-max normalization rescales the feature to a fixed range, typically 0 to 1, or it can be shifted to any range \([a, b]\) by transforming the data according to the formula: \[ x' = \frac{(x - \text{min}(x))}{(\text{max}(x) - \text{min}(x))} \times (\text{max} - \text{min}) + \text{min} \] Where \(x\) is the original value, \(\text{min}(x)\) is the minimum value for that feature, \(\text{max}(x)\) is the maximum value, and \(\text{min}\) and \(\text{max}\) are the new minimum and maximum values for the scaled data. Implementing these scaling techniques will ensure that the features contribute equally to the development of the model and improve the convergence speed of learning algorithms.

import numpy as np

def feature_scaling(data):
    # Standardization
    mean = np.mean(data, axis=0)
    std = np.std(data, axis=0)
    standardized_data = (data - mean) / std
    
    # Min-Max Normalization
    min_val = np.min(data, axis=0)
    max_val = np.max(data, axis=0)
    normalized_data = (data - min_val) / (max_val - min_val)
    
    return np.round(standardized_data,4).tolist(), np.round(normalized_data,4).tolist()