Hooray! We have completed the first part of the advanced feature of this book; that is, machine learning with Elasticsearch. We have introduced the machine learning feature of the Elastic Stack. We created a single-metric job to track the volume field to detect anomalies in the data of the cf_rfem_hist_price index. We have also introduced the Python scikit-learn library and the unsupervised learning algorithm, k-means clustering. The KMean class is provided in the sklearn.cluster package. We have extracted data from the cf_rfem_hist_price index and used three fields, changeOverTime, changePercent, and volume, to construct multidimensional input data, in order for the k-means clustering to find the anomalies. By using the matplotlib.pyplot() function, we have plotted a graph to show the anomalies and the regular data.
In the next chapter, we will provide an overview of...
