From the course: Python for Data Science and Machine Learning Essential Training Part 2
Join today to access over 25,300 courses taught by industry experts.
Cluster analysis with the K-means method - Python Tutorial
From the course: Python for Data Science and Machine Learning Essential Training Part 2
Cluster analysis with the K-means method
- [Instructor] K-means clustering is an unsupervised machine learning algorithm that you can use to predict subgroups within a dataset. With K-means clustering, you usually have an idea of how many subgroups are appropriate. Use cases for K-means clustering include market price and cost modeling, insurance claim fraud detection, hedge fund classification, and customer segmentation. The K-means clustering algorithm is a simple unsupervised algorithm that's used for quickly predicting groupings from within an unlabeled dataset. Predictions are based on the number of centroids present as represented by the unit k and nearest mean values, given a Euclidean distance measurement between observations. Some things to keep in mind when using K-means. You'll need to scale your variables and you'll want to go ahead and look at a scatter plot or the data table to estimate the appropriate number of centroids to use for the k…
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.