Correlation exposes the relationship between variables. We can use correlation to understand whether changing the values for variable A is likely to also change the values for variable B. This article explains how correlation works, the different types of correlation alongside pointing out how to measure degrees of correlation in Graphext.

After you build a prediction model, you'll find the Models panel in your project letting you inspect how your model was built and how it performed. This article helps you interpret the information inside the three sections of your Models panel.

Inside Models, you'll find the - General - Training - Result - tabs. These contain information helping you to grasp how your model was built and how accurate it was.

This guide is intended to walk through the building of a market basket analysis project using Graphext. We will be working with a dataset of purchases made at a bakery in Edinburgh. The aim of the project will be to identify communities of products that are related to one another and to transform this information into useful business insights.

You can train and deploy prediction models in Graphext. Models use existing features of your data to predict the value of a target variable.