Machine learning is a branch of computer science that deals with pattern recognition to develop artificial intelligence. The intelligence thus studies and generates algorithms that can be used to make precise predictions on the given dataset. Machine learning can be used and implemented to analyze public interest from social media data and make pricing decisions using data-driven statistics for an e-commerce website. Thus, machine learning can be very useful to track the given data and reach to a conclusion for business decisions. Machine learning can be effectively implemented on data financial services, media, retail, pharmaceuticals, telecom, security, and so on.

You already know the Splunk commands for predicting and trending, but now, you will learn how machine learning can be effectively applied on the data using Splunk and apps from the Splunk app store.

The process of machine learning is explained in the following diagram:

The preceding flowchart can be explained and understood well using the following example:

The Splunk machine learning command fit (modeling using training), apply (learning), and Summary (prediction)) can be used to implement machine learning. The Splunk app ML Toolkit needs to be installed to implement machine learning.

The Splunk ML Toolkit app provides the end user with the facility to create a model, refine the model, apply to make a prediction, and detect anomalies for better and efficient prediction. The toolkit app has various examples of machine learning implemented using numerical and categorical values.

Thus, using the Splunk app, artificial intelligence about the data can be developed and crucial business decisions can be taken beforehand by predicting the condition based on historical data.