Normalization vs Standardization Technique in Data Science

In the world of data science, preparing data for analysis is as crucial as the analysis itself. Two common techniques used in data preprocessing are normalization and standardization. Both methods serve to adjust the values of numerical data so that they fall within a certain range, but they do so in different ways and are suited for different purposes. This article aims to simplify these concepts and explain their use cases in detail.

Introduction

What is Normalization?

Normalization, also known as min-max scaling, is the process of transforming data to fit within a specific range, typically between 0 and 1. The formula for normalization is:

Where X is the original data value, X_min is the minimum value in the data set, and X_max is the maximum value in the data set. This technique is particularly useful when the data does not follow a Gaussian (normal) distribution and is skewed.

What is Standardization?

Standardization, also known as z-score normalization, transforms data to have a mean of 0 and a standard deviation of 1. The formula for standardization is:

Where X is the original data value, μ is the mean of the data set, and σ is the standard deviation of the data set. This method is useful when the data follows a Gaussian distribution and you want to maintain the properties of the original data distribution.

Use Cases

When to Use Normalization

Normalization is beneficial in the following scenarios:

1. Machine Learning Algorithms: Algorithms like K-Nearest Neighbors (KNN) and Neural Networks are distance-based and sensitive to the scale of the data. Normalization ensures that all features contribute equally to the result.
2. Image Processing: When dealing with pixel values in image data, normalization helps in compressing the range of pixel values, making the processing faster and more efficient.

When to Use Standardization

Standardization is preferred in these situations:

1. Statistical Models: Models like Linear Regression, Logistic Regression, and Principal Component Analysis (PCA) assume that the data is normally distributed. Standardization helps in meeting this assumption, thus improving model performance.
2. Comparative Analysis: When you need to compare data points that are on different scales, standardization helps by bringing them to a common scale with mean 0 and standard deviation 1.

Practical Implementation

Normalization Example

Let's normalize a simple data set using Python:

from sklearn.preprocessing import MinMaxScaler
import numpy as np

data = np.array([[1], [2], [3], [4], [5]])
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(data)
print(normalized_data)        

Standardization Example

Let's standardize the same data set using Python:

from sklearn.preprocessing import StandardScaler
import numpy as np

data = np.array([[1], [2], [3], [4], [5]])
scaler = StandardScaler()
standardized_data = scaler.fit_transform(data)
print(standardized_data)        

Conclusion

Both normalization and standardization are essential techniques in data preprocessing. The choice between them depends on the nature of the data and the requirements of the specific machine learning model being used. Normalization is suitable for non-Gaussian, skewed data, while standardization is ideal for data that follows a normal distribution and for models that assume normally distributed data.

Understanding when and how to apply these techniques can significantly enhance the performance of your machine learning models, leading to more accurate and reliable predictions. By mastering these preprocessing steps, you can ensure that your data is in the best possible shape for analysis.

Read More about Normalization and Standardization:

1. https://www.geeksforgeeks.org/normalization-vs-standardization/
2. https://www.simplilearn.com/normalization-vs-standardization-article
3. https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/
4. https://towardsdatascience.com/normalization-vs-standardization-explained-209e84d0f81e