You'll be using the credit card customer data. Before clustering the data, you should follow these steps:

1.  **Load the data:** use pandas to load the CSV file; 

2.  **Handle missing values:** if necessary, impute or remove rows with missing data; 

3.  **Feature scaling:** apply `StandardScaler` to scale the features. This is important because hierarchical clustering uses distance calculations;     

4.  **Dimensionality reduction (PCA):** apply principal component analysis (PCA) to reduce the data to two dimensions. This will make it easier to visualize the clusters. 

## Interpreting the Dendrogram

First, you should analyze the **dendrogram** to determine a suitable number of clusters. Look for large vertical distances that are not crossed by any extended horizontal lines. 

Next, you can plot the data points after PCA, coloring them according to the **cluster labels** obtained by cutting the dendrogram at the chosen height. 

Finally, you should examine the characteristics of the resulting clusters. It is recommended to look at the **average values of the original features** (before PCA) for each cluster to understand how the clusters differ. 

## Conclusion 

Hierarchical clustering is a powerful technique when you **don't want to pre-specify the number of clusters** or when you need to understand the **hierarchical relationships** between data points. However, it can be **computationally expensive** for very large datasets, and choosing the right linkage method and the optimal number of clusters requires careful consideration and often involves a combination of quantitative methods and domain expertise. 

Le clustering est l'une des techniques fondamentales de l'apprentissage automatique qui vous permet de résoudre de nombreux problèmes complexes dans la vie réelle : segmentation de marché, détection d'anomalies, réduction de la dimensionnalité, révélation de motifs cachés, etc.

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.

Analyse de Cluster

Implementing on Customers Dataset

Interpreting the Dendrogram

Conclusion