Question

Grouping mammal sleep habits using k-means clustering the msleep dataset contains information on sleep habits for 83 mammals. features include total sleep, length of the sleep cycle, time spent awake, brain weight, and body weight. animals are also labeled with their name, genus, and conservation status. load the dataset into a data frame. create a new data frame x with sleep total and sleep cycle. initialize a k-means clustering model with 4 clusters and random state.

Which of the following steps is involved in grouping mammal sleep habits using k-means clustering on the msleep dataset?
A) Load the dataset into a data frame.
B) Create a new data frame x with sleep total and sleep cycle.
C) Initialize a k-means clustering model with 4 clusters and random state.
D) All of the above.

Answer 1

The process of grouping mammal sleep habits using k-means clustering on the msleep dataset involves loading the dataset into a data frame, creating a new data frame with sleep total and sleep cycle, and initializing a k-means model with 4 clusters and a random state.

Step-by-step explanation:

To perform the clustering, you need to: A) Load the dataset into a data frame. This involves reading the data from a file or database and creating an in-memory table that will allow you to manipulate and analyze the data. B) Create a new data frame, let's call it X, that includes only the variables 'sleep total' and 'sleep cycle', as these are the features you want to use for clustering. Finally, C) Initialize a k-means clustering model specifying the number of clusters you want to create (4 in this case) and set a random state for reproducibility.

The k-means algorithm will then assign each mammal to one of the four clusters based on their sleep patterns, with the goal of minimizing variance within clusters and maximizing variance between clusters.