Python - Python Machine Learning - Standard Deviation Part 4: Standard Deviation in Data Normalization

Machine learning models work better when data is standardized. A common technique is Z-score normalization, where each value is transformed as:

Z = (x - x̄) / σ

Example 5: Standardizing Data Using Standard Deviation

import numpy as np

data = [10, 20, 30, 40, 50]

mean = np.mean(data)

std_dev = np.std(data)

normalized_data = [(x - mean) / std_dev for x in data]

print("Normalized Data:", normalized_data)

Output:

Normalized Data: [-1.414, -0.707, 0.0, 0.707, 1.414]

Explanation:

After normalization, the transformed values have a mean of 0 and a standard deviation of 1.

Standardization helps ML algorithms work consistently.