In the dynamic world of machine learning, the quality of your data often determines the effectiveness of your models. One crucial step in preprocessing is data normalization, a method used to scale input data into a uniform range.

Data normalization in machine learning ensures that features with varying scales contribute equally to the model’s training process, leading to improve accuracy and performance.

Whether you are working on regression models or clustering problems, data normalization plays a pivotal role. This article explores the fundamental techniques of data normalization, difference between fundamental concepts like normalization and standardization along with the advantages and disadvantages of normalization of machine learning.

What is Data Normalization ?

Data normalization is a technique used to organize data in a way that reduces duplicate data (redundancy) and ensures consistency in the process. It’s particularly important concept for databases and machine learning to make data more structured, efficient and easier to analyze.

In this blog we are considering two common contexts to understand data normalization :

Normalization in Databases

In databases, it’s about dividing large tables into smaller related tables, and linking them using relationship to avoid duplicate data.

Normalization in Machine Learning

In machine learning, its about scaling numerical data to bring all features to a similar range (e.g, 0 to 1) for better performance of algorithms.

Example 1 : Normalization in databases

Let’s make a messy dataset with unnecessary duplicate unnormalized table:

Emp ID	Names	Dept.	Manager	Email ID
1	Emp A	Sales	A	sales@manager.com
2	Emp B	Sales	A	sales@manager.com
3	Emp C	Technical	B	tech@manager.com

Here is the issue or problem in this table : Manager’s information is repeated for every employee in the same department and if the manager’s detail change, you must update multiple rows.

After Normalization

We are splitting the data into two table.

• Employee Table

Employee ID	Name	Department ID
1	Emp A	1001
2	Emp B	1001
3	Emp C	1002

• Department Table

Dept. ID	Dept.	Manager	Email ID
1001	Sales	A	sales@manager.com
1002	Technical	B	tech@manager.com

No Redundancy : Manager information gets stored once after normalization.

Easy Update : Changing the manager’s information only require adding one row.

Example 2 : Data Normalization in Machine Learning

While working with ML models, features may have different scales. For instance: Original Data

Age	Income(s)
30	50,000
60	100,000

Here is the income range that is much larger than age. If left unnormalized, models may focus more on income than age.

Skewing predictions:

After Normalization (Scaling Data to Range 0 – 1)

Age(Scaled)	Income(Scaled)
0.0	0.0
1.0	1.0

For Normalized Data : step by step calculation here

Normalization Formula : x_scaled = (x – min (x) ) / ( max (x) – min (x) )

Data Normalization Techniques

Data normalization in machine learning involves scaling or transformation data so that all features have comparable ranges or distributions. This helps models work more effectively , especially when features carries different scales.

Here are the most common data normalization techniques that are explained below :

Min Max Scaling (Feature Scaling)

What it does :

• Scales the data to fit within a specific range, typically between 0 and 1.
• It preserves the relative relationships between values by proportionally adjusting them accordingly.

Normalization Formula : x_scaled = (x – min (x) ) / ( max (x) – min (x) )

In this formula : x is the original value, min(x) is the minimum value in the dataset and max(x) is the maximum value in the dataset.

Standardization Scaling ( Z-Score )

What it does :

• Transform the data to have a mean of 0 and standard deviation of 1.
• It makes the data follow a standard normal distribution.

Formula :
Where: z = (x – μ) / σ

x is the original value.

μ is the mean of the dataset.

σ is the standard deviation of the dataset.

Key Characteristics:

Range: The transformed values are not bounded (they can range from negative infinity to positive infinity).

Effect on Outliers: More robust than Min-Max for datasets with outliers, but extreme values may still have some influence.

	Min Max Scaling	Standardization Scaling
	There is no strict assumptions made here about data distribution	Data is normally distributed among standardization scaling
	Neural networks , K-Nearest Neighbor	Principal Component Analysis, Support Vector Machines, and Logistic Regression
	Min-Max scaling is sensitive to outliers	Standardization scaling handles outliers better
	[0,1] (fixed range)	Mean=0 and standard deviation =1

Why do we normalize data in machine learning?

Normalization in machine learning means adjusting data so all features are on the same scale. It is like resizing object so that they fit into the same box , making them easier to compare.

Why Normalize ?

1. Equal importance : If one feature has larger numbers (like salary in thousands and another has smaller ones (like age in tens), the model might pay more attention to the big numbers. Normalization balances this.
2. Faster Learning : Algorithms like gradient descent work better and faster when data is scaled evenly.
3. Fair Distances : Models that calculate distance (like KNN or K-means) need all features to contribute fairly, not just the ones with big values.

Examples : Imagine if you are comparing a person’s height (in meters) and weight (in kilograms) without normalization, weight (e.g, 75) might overshadow height (e.g 1.75) just because it has bigger numbers. Normalizing puts both on the same level.

Difference between normalization and standardization

	Normalization	Standardization
	In normalization we are rescaling data to a specific range (e.g, to 0 to 1).	In standardization, we are transforming data to have a mean of 0 and standard deviation of 1.
	Normalization brings all the value in a common scale.	It ensures that data follows a standard gaussian distribution(bell curve).
	It ensures the range between 0 and 1 or 1 and -1.	No fixed range, highly depends on a data distribution.
	Normalization is sensitive to outliers ( it can distract the range).	Standardization is less sensitive in comparison to normalization, but still impacted
	When the data doesn’t allows a gaussian distribution or needs to be scaled to a specific range(e.g, image processing, neural networks).	When data is normally disturbed and you need to maintain statistical properties ( e.g, SVM and PCA).
	KNN, Neural Network and Logistic Regression	SVM, PCA and Linear Regression

Advantages of data normalization in machine learning

• Normalization ensures features contribution equally to the machine learning models, preventing bias due to large scale feature differences.
• Scaling data helps gradient based optimization algorithms converge more quickly.
• Normalization improves the algorithms like K-Nearest Neighbors or Support Vector Machines that rely on distance calculations.

Disadvantages of data normalization in machine learning

• Normalization can be heavily affected by extreme values, distorting the whole scaling process.
• Rescaled values may lose their original meanings, making it harder to interpret feature contribution.
• Normalization is unnecessary for tree based algorithms like decision trees and random forest.

Frequently Asked Questions (FAQs)

Why is data normalization important ?

Data normalization is a key step in machine learning that adjusts the scale of features to ensure they are comparable. Without normalization, features with larger values can overpower those with smaller values, leading to biased model predictions. By putting all the features on the same scale, normalization improves the accuracy of the model which makes training faster and more stable.

When should I normalize data?

You should normalize data when your features have different units or scales (like kilometers, kilograms, age, heights) etc. It is also important if you are using algorithm like KNN, SVM, logistic regression and neural network that are sensitive to the data scale. Normalization helps speed up training process and improve results for distance-based or optimization algorithms.

Why do we use normalization techniques ?

We use normalization technique to make sure that all data is on a similar scale. This is important when different features have very different ranges (like one feature being in the thousands and other in single digits), it can make it hard for algorithms to work well. Normalization helps the algorithms treat all the features, speeding up the training, making up the model more accurate.

How does normalization affect machine learning model ?

Normalization affects machine learning model by making all features on the same scale, so no feature with larger values dominates smaller ones. This leads to better model accuracy and stable training. It also helps distance-based algorithms like KNN and SVM calculate distance more accurately, improving their performance. Overall normalization ensures the model learns effectively and gives reliable results.

What is the impact of normalizing categorical data ?

Normalizing categorical data can help make sure all features are on a similar scale, which can be helpful for some models, like neural networks or K-Nearest Neighbors (KNN). It ensures that no single category with a higher value dominates the learning process, improving model performance and speeding up training.

However, normalizing categorical data can also make it harder to understand or interpret the results since the encoded values might lose their meaning. If done incorrectly, like applying inappropriate scaling methods, it can distort the data and affect the model’s accuracy.

What are outliers, and how do they affect normalization ?

Outliers are data points that are much higher or lower than most others in the dataset. They can affect normalization by distorting the scale, making most values compressed into a narrow range, or by influencing the statistical measures like mean and standard deviation. This can lead to poor performance. To minimize this impact, it is important to remove or transform outliers before normalizing the data.

Does normalization affect the interpretability of the data ?

Yes, normalization can affect the interpretability of data. When data is normalized especially using techniques like min max scaling and Z-Score normalization, the original scale and units of the features are lost. This makes it harder to directly interpret the meaning of the transformed values. For example: a feature that originally represented ‘age’ in years might lose its clear meaning after normalization. However normalization is necessary for model performance and techniques like inverse transformation can help interpret the result if needed.