Clustering Model and Prediction

Learn how to use H2O's KMeans algorithm for building insightful clustering models.

In this lesson, we’ll cover the implementation of the H2OKMeansEstimator algorithm, including how it works, the key parameters involved, and how to interpret the results. We’ll learn how to preprocess the RFM dataset, apply the clustering algorithm using H2OKMeansEstimator, and visualize the clustering results. By the end of this lesson, we’ll have the knowledge and practical skills to perform clustering on different datasets and uncover valuable insights for the business or research. Let’s dive in and learn about the clustering models in detail.

Training H2OKMeansEstimator

Let’s work with the RFM dataset to build a clustering model leveraging the H2OKMeansEstimator algorithm from the H2O library. This dataset provides information on retail customers and their purchasing history commonly used in marketing and retail analytics. The dataset consists of Recency, Frequency, and Monetary columns. Let’s have a look:

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