Imagine you have a bunch of toys, each with different sizes. Now, let’s say you want to make sure they all fit in the same toy box. Normalization in machine learning is a bit like that. It’s like making sure all the toys (or features) in your dataset are on the same scale, between 0 and 1.

To do this, we use a magic formula called normalization. It’s like a secret recipe that changes the size of each toy so they all fit nicely in the toy box. The formula goes like this: Xi’ = (Xi – Xmean) / (Xmax – Xmin). Don’t worry if it sounds a bit like a spell – it’s just a way to make sure everything is in order.

Now, there are different ways to use this magic. We have MinMaxScaling, Mean Normalization, Max Absolute, and even Robust Scaling. Each method has its own way of making sure our toys play nicely together.

But here’s the cool part: not all toys need this special treatment. Some, like Decision Trees, Random Forest, Gradient Boosting, and XGBoost, are already good friends and don’t get affected by this magic resizing.

Let’s break it down even more. Imagine you have a list of toys – let’s call it “wine_data.csv.” Some toys are labeled with things like “Class label,” “Alcohol,” and “Malic acid.” Now, to make sure our toys are ready for play, we split them into two groups: one for practice and one for a big game day.

We use a cool trick called Train Test Split to do this. It’s like having two teams – one practices and the other plays the game. But remember, our toys are special, so we need to use the magic formula to get them all set up.

Our friend uses a special tool called a Normalizer to help with this. It’s like having a toy shrink-ray! They use it on the practice team (training set) first and then on the game team (test set) to make sure everyone is the right size.

Now, if you want to see the magic in action, there’s a part where you can actually see the toys before and after the resizing. It’s like looking at a photo album – one page shows the toys before, and the other page shows them after the magic.

And that’s it! Just like making sure all your toys are the perfect size for your toy box. Easy, right?



Normalization is a fundamental concept in machine learning, specifically referring to the process of scaling the features of a dataset between 0 and 1. This crucial step is often employed to enhance the performance of machine learning algorithms, as many of them assume that input data features are on the same scale.

Why Normalize Data?

The uniform scaling of features becomes imperative when dealing with machine learning algorithms. This ensures that each feature contributes proportionally to the result without being overshadowed by features with larger scales. The goal is to maintain the integrity of differences in the ranges of values while preventing the loss of essential information.

Normalization Formula

The formula for normalization, Xi’ = (Xi-Xmean)/(Xmax-Xmin), encapsulates the essence of scaling features to a common range, typically between 0 and 1. Understanding this formula is pivotal in grasping the normalization process.

Before and After Scaling

Visualizing the effects of normalization is enlightening. By comparing data before and after scaling, we can observe the impact on the distribution of features. The provided code allows you to witness these effects firsthand.

Types of Normalization

  1. MinMaxScaling
  • Xi ‘ = (Xi-Xmin)/(Xmax-Xmin)
  1. Mean Normalization
  • Xi’ = Xi-Xmean/Xmax-Xmin
  1. Max Absolute
  • Xi’ = Xi/|Xmax|
  1. Robust Scaling
  • Xi’ = Xi-Xmedian/IQR(75%-25%)

You can access the code Here :

Normalization in Machine Learning Models

Normalization is a crucial preprocessing step before training machine learning models. It becomes particularly beneficial when dealing with datasets where the scale of features varies significantly. It’s essential to apply normalization to the training set and then use the same parameters for the test set to ensure consistent scaling.

Types of Machine Learning Algorithms Unaffected by Standardization

Interestingly, some machine learning algorithms, such as Decision Trees, Random Forest, Gradient Boosting, and XGBoost, are not affected by standardization. Understanding these exceptions is valuable in selecting the right preprocessing techniques.


In conclusion, normalization plays a vital role in ensuring that machine learning models perform optimally. By scaling features to a common range, we enable algorithms to make accurate predictions without biases introduced by varying scales.

#you will see the effects before and after scaling after you run this (Normalization) code.


  • Why is normalization important in machine learning?

Normalization ensures that features contribute proportionally to machine learning algorithms, preventing biases introduced by varying scales.

  • Can normalization be applied directly to the test set?

Normalization should be applied to the training set, and the same normalization parameters should be used for the test set for consistent scaling.

  • Are all machine learning algorithms affected by normalization?

No, algorithms like Decision Trees, Random Forest, Gradient Boosting, and XGBoost are not affected by normalization.

  • How does normalization improve algorithm performance?

Normalization prevents features with larger scales from overshadowing others, ensuring each feature contributes proportionally to the result.

  • What is the role of the MinMaxScaler in normalization?

MinMaxScaler is used to scale features to a specific range, typically between 0 and 1, facilitating normalization.