Feature Selection using Hierarchical Clustering | Python Tutorial

In this comprehensive Python tutorial, we delve into feature selection for machine learning with hierarchical clustering. We guide you through the essentials of partitioning features into cohesive groups to minimize redundancy in model training. This technique is particularly important as your dataset expands, offering a structured alternative to manual grouping. What you'll learn: The importance of variable clustering algorithms in handling large feature sets. Detailed application of hierarchical clustering to form intuitive feature groups with a focus on Ward’s distance metric. Visualising clusters using a Dendrogram A comparative analysis highlighting the advantages of hierarchical clustering over other clustering methods. Insights into the method's output using correlation heatmaps, demonstrating the formation of homogeneous feature groups. Why is this important? In the data-driven industry, navigating through hundreds or thousands of potential features in your dataset is a challenge. While dimensionality reduction methods like PCA offer a solution, the result is hard-to-interpret features. Hierarchical clustering emerges as a hero, paving the way for an interpretable model with a concise feature list. 🚀 Free Course 🚀 Signup here: https://mailchi.mp/40909011987b/signup XAI course: https://adataodyssey.com/courses/xai-... SHAP course: https://adataodyssey.com/courses/shap... 🚀 Companion article with link to code (no-paywall link): 🚀 https://medium.com/data-science/featu... 🚀 Useful playlists 🚀 XAI:    • Explainable AI (XAI)   SHAP:    • SHAP   Algorithm fairness:    • Algorithm Fairness   🚀 Get in touch 🚀 Medium:   / conorosullyds   Threads: https://www.threads.net/@conorosullyds Twitter:   / conorosullyds   Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Introduction 00:50 What is feature selection? 01:37 Theory of Hierarchical Clustering 06:35 Applying Hierarchical Clustering 09:31 Visualising the Dendrogram 10:43 Feature selection 13:23 Sense-checking clusters