Clustering with PyCaret

Explore clustering methods using PyCaret, focusing on K-Means and other algorithms to segment datasets by shared features. Understand the setup, synthetic dataset generation, and model evaluation to build a foundational clustering skillset for unsupervised machine learning tasks.

We'll cover the following...

Clustering methods in PyCaret
Importing the necessary libraries
Generating a synthetic dataset

One of the fundamental tasks in unsupervised machine learning is clustering. This task aims to categorize instances of a given dataset in different clusters based on their common characteristics. Clustering has many practical applications in various fields such as market research, social network analysis, bioinformatics, medicine, and others. The k-means clustering method is a simple and widely used method. It is defined in the following formula:

\min_{C_{1}, \ldots, C_{K}}\left\{\sum_{k=1}^{K} W(C_{k})\right\}

$K$ is the number of all clusters, while $C_{k}$ represents each cluster. Our goal is to minimize ...

1.Introduction to Machine Learning

2.Regression

3.Classification

4.Clustering

Project

5.Anomaly Detection

6.Natural Language Processing

7.Deploying a Machine Learning Model

8.Conclusion

9.Appendix

Clustering with PyCaret