Cluster analysis is an unsupervised machine learning method that partitions the observations in a data set into a smaller set of clusters where each observation belongs to only one cluster. The goal of cluster analysis is to group, or cluster, observations into subsets based on their similarity of responses on multiple variables. Clustering variables should be primarily quantitative variables, but binary variables may also be included. In this session, we will show you how to use k-means cluster analysis to identify clusters of observations in your data set. You will gain experience in interpreting cluster analysis results by using graphing methods to help you determine the number of clusters to interpret, and examining clustering variable means to evaluate the cluster profiles. Finally, you will get the opportunity to validate your cluster solution by examining differences between clusters on a variable not included in your cluster analysis.
You can use the same variables that you have used in past weeks as clustering variables. If most or all of your previous explanatory variables are categorical, you should identify some additional quantitative clustering variables from your data set. Ideally, most of your clustering variables will be quantitative, although you may also include some binary variables. In addition, you will need to identify a quantitative or binary response variable from your data set that you will not include in your cluster analysis. You will use this variable to validate your clusters by evaluating whether your clusters differ significantly on this response variable using statistical methods, such as analysis of variance or chi-square analysis, which you learned about in Course 2 of the specialization (Data Analysis Tools). Note also that if you are working with a relatively small data set, you do not need to split your data into training and test data sets.

Learned some really useful ML models.