Implementing on Real Dataset

You'll use the mall customers dataset, which contains the following columns:

You should also follow these steps before clustering:

Load the data: you'll use pandas to load the CSV file;
Select relevant features: you'll focus on 'Annual Income (k$)' and 'Spending Score (1-100)' columns;
Data scaling (important for DBSCAN): since DBSCAN uses distance calculations, it's crucial to scale features to have similar ranges. You can use StandardScaler for this purpose.

Interpretation

The code creates 5 clusters in this case. It's important to analyze the resulting clusters to gain insights into customer segmentation. For example, you might find clusters representing:

High-income, high-spending customers;
High-income, low-spending customers;
Low-income, high-spending customers;
Low-income, low-spending customers;
Middle-income, middle-spending customers.

Concluding Remarks

War alles klar?

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Abschnitt 5. Kapitel 5

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Kursinhalt

Clusteranalyse

1. Clustering Fundamentals

Introduction to Clustering Clustering Vs Classification Clustering Algorithms and Libraries

2. Core Concepts

Missing Values Handling Categorical Features Encoding Data Normalization Distance Measures Linkages Challenge: Preprocessing the Dataset

3. K-Means

4. Hierarchical Clustering

How Hierarchical Clustering Works?Optimal Number of Clusters Implementing on Dummy Dataset Implementing on Customers Dataset Challenge: Implementing Hierarchical Clustering

5. DBSCAN

Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing DBSCAN

6. GMMs

Problem Statement What is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy Data Implementing GMM on Real Data Challenge: Implementing Gaussian Mixture Models Conclusion