Course Description
These lessons introduce the most common style of clustering, k-means, and work through a detailed use case of a customer segmentation. You’ll learn how to choose input variables, select the number of clusters, and interpret the centroids. You’ll learn how to assess the quality of a cluster analysis model, and how to address computational challenges that come with clustering. We’ll examine how to create personas and methods for classifying new data.
Finally, this course will introduce the concept of the K-means algorithm and centroid interpretation. Throughout this course, you’ll learn about input variables, model quality metrics, and classifying new data. You’ll also learn about some computational challenges that you’ll find with K-means clustering.
What You'll Learn
- Apply the K-means algorithm to build a customer segmentation model
- Choose input variables and select the number of clusters for a cluster analysis
- Interpret centroids and create personas for marketing or product development
- Assess model quality using validation methods and quality metrics
- Address computational challenges that arise with K-means clustering
- Classify new data using the trained clustering model
Key Takeaways
- K-means is presented as the most common style of clustering and is taught through a detailed customer segmentation use case.
- Building a segmentation model involves choosing input variables, selecting the number of clusters, and interpreting the centroids.
- The course covers how to assess the quality of a cluster analysis model using quality metrics and validation methods.
- Clustering introduces computational challenges that the course explains how to address.
- Centroid interpretation supports creating personas that can be used in marketing or product development and classifying new data.
Frequently Asked Questions
What does this course cover?
It introduces k-means, the most common style of clustering, and works through a detailed customer segmentation use case, including choosing input variables, selecting the number of clusters, interpreting centroids, assessing model quality, addressing computational challenges, creating personas, and classifying new data.
What skills will I gain from this course?
The course builds skills in cluster analysis, hierarchical clustering, K-means clustering, market segmentation, segmentation analysis, and spectral clustering.
What lessons are included?
The lessons are: The K-means Algorithm, Choosing Input Variables, Choosing the Number of Clusters, Centroid Interpretation, Model-Quality Metrics, Computational Challenges, Creating Personas, and Classifying New Data.
How can the segmentation results be used?
The course shows how to create personas for use in marketing or product development and how to classify new data.
