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

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Seksjon 5. Kapittel 5

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Kursinnhold

Cluster Analysis

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