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hands-on lab

Performing K-Means Clustering With Python

Difficulty: Intermediate
Duration: Up to 1 hour
Students: 237
Rating: 4/5
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Description

K-Means learning is a machine learning technique used to divide a dataset into clusters to analyze its results. This classification algorithm divides a large group of data into smaller groups to maximize the similarity between data points. We will walk through applying and analyzing the K-Means clustering algorithm on a set of data using the Python libraries: pandas, scikit-learn, and matplotlib.

Learning Objectives

Upon completion of this lab you will be able to:

  • Utilize Python to prepare data for Cluster Machine Learning
  • Perform K-Means Clustering on a set of data
  • Plotting the outcome of the K-Means clustering

Intended Audience

This lab is intended for:

  • Data engineers
  • Machine learning practitioners
  • Anyone interested in using Python to perform clustering

Prerequisites

You should possess:

  • A basic understanding of Python
  • A basic understanding of K-Means Clustering

Updates

August 8th, 2024 - Added password protection to notebook

Covered topics

Development
Python

Lab steps

Signing In to the Google Cloud Console
Opening the Lab's Jupyter Notebook in Google Cloud

Lab Rules