A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns…

Question

Options

• AEmbed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE)
• BRun k-means using the Euclidean distance measure for different values of k and create an elbow
• CEmbed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE)
• DRun k-means using the Euclidean distance measure for different values of k and create box plots

• A
  69% (25)
• B
  6% (2)
• C
  8% (3)
• D
  17% (6)

Explanation

t-SNE (t-distributed Stochastic Neighbor Embedding) is the best choice here for two reasons: it performs nonlinear dimensionality reduction to 2D or 3D, and the resulting scatter plot visually reveals natural clusters in the data without needing to pre-specify the number of groups. With thousands of columns and hundreds of numerical features, t-SNE is particularly powerful for high-dimensional visualization. k-means (options B and D) requires you to specify k in advance and produces cluster assignments rather than an intuitive visual - it is better suited when you already suspect a number of clusters. An elbow plot helps choose k but still requires running many k-means iterations. t-SNE gives both cluster discovery and visualization in a single step, satisfying 'as quickly as possible.'

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