**Data normalization** is a critical preprocessing step for many clustering algorithms, including K-means. Features in real-world datasets often have different scales and units. Algorithms that rely on **distance calculation**s, like K-means, can be heavily influenced by features with larger scales. Normalization aims to bring all features to a **similar scale**, preventing features with larger values from dominating the clustering process. 

## StandardScaler 

`StandardScaler` standardizes features by removing the mean and scaling to unit variance. It transforms data to have a **mean of 0** and a **standard deviation of 1**. This is achieved by subtracting the **mean** and dividing by the **standard deviation** for each feature. 

`StandardScaler` is effective when your data is approximately **normally distributed**. It is widely used and often a good default normalization method for many algorithms.

```python
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
features = df[['feature1', 'feature2']]

df[['feature1', 'feature2']] = scaler.fit_transform(features)
```

## MinMaxScaler 

`MinMaxScaler` scales features to a specific range, typically between **0** and **1**. It transforms data by scaling and shifting each feature individually so that it is within the given range. 

`MinMaxScaler` is useful when you need values within a **specific range**, or when your data is not normally distributed. It preserves the shape of the **original distribution**, just scaled to the new range. 

```python
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
features = df[['feature1', 'feature2']]

df[['feature1', 'feature2']] = scaler.fit_transform(features)
``` 

Choosing between `StandardScaler` and `MinMaxScaler` depends on your data and the specific algorithm. `StandardScaler` is often preferred for algorithms like **K-means** when features are roughly **normally distributed**. `MinMaxScaler` can be useful when you need **bounded values** or when data is not normally distributed.

Why is data normalization important when using clustering algorithms like K-means?

Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.

Dive into the fundamentals of clustering and discover how it differs from classification. Explore essential algorithms, tools, and libraries that power this unsupervised learning technique to uncover hidden patterns in data.

Gain a solid understanding of key preprocessing techniques that ensure effective clustering. Learn how to handle missing values, encode categorical features, normalize data, and choose appropriate distance measures and linkages to boost clustering accuracy.

Master the skills needed to apply K-Means clustering effectively. Learn how the algorithm works, determine the optimal number of clusters, and gain hands-on experience by implementing K-Means on both synthetic and real-world datasets.

Explore the essentials of hierarchical clustering and learn how to group data into meaningful clusters using dendrograms. Build confidence in identifying the optimal number of clusters and implementing the technique on both synthetic and real-world datasets.

Discover how DBSCAN excels at detecting clusters of varying shapes and handling noise in data. Learn the mechanics behind this density-based algorithm, how to assign points to clusters, and apply it to both synthetic and real datasets with confidence.

Gain a solid understanding of Gaussian Mixture Models and how they use probability to model complex cluster shapes. Learn the principles of Gaussian distribution, explore how GMMs work, and build confidence by applying them to both dummy and real-world data.

Cluster Analysis

Data Normalization

StandardScaler

MinMaxScaler