In this tutorial, we will talk about four clustering model algorithms, compare their results, and discuss how to choose a clustering algorithm for a project. You will learn:
👉 What are the different types of clustering model algorithms?
👉 How to run K-means, Gaussian Mixture Model (GMM), Hierarchical model, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) model in Python?
👉 How to use PCA (Principal Component Analysis) and t-SNE (t-distributed stochastic neighbor embedding) for dimensionality reduction and visualization?
👉 How to utilize clustering model results for the business?
👉 How to select a clustering model algorithm for your project?
⏰ Timecodes ⏰
0:00 - Intro
0:33 - Step 0: Clustering Model Algorithms
1:22 - Step 1: Import Libraries
2:26 - Step 2: Read Data
3:39 - Step 3: Decide the Number of Clusters
4:12 - Step 4: Kmeans Clustering (Model 1)
5:39 - Step 5: Hierarchical Clustering (Model 2)
6:46 - Step 6: Gaussian Mixture Model (GMM) (Model 3)
8:09 - Step 7: Density-based spatial clustering of applications with noise (DBSCAN) (Model 4)
9:41 - Step 8: Dimensionality Reduction
10:00 - Step 9: Visual Comparison of Models
11:39 - Step 10: Utilize Clustering Model Results for Business
12:33 - Step 11: Which Model to Use?
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