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Clustering is a machine learning technique used to group data points based on similarity. This guide is intended to walk you through the process of creating a clustering model to group your data. We'll be using a dataset of 1000 supermarket transactions from stores in Myanmar. The aim of our project is to group these transactions in order to find patterns in the buying habits of the supermarket's customers.
This guide is intended to walk you through the process of building maps in Graphext. We'll look at the different types of maps you can create and the variables you need to do so before moving on to plot 37012 Airbnb listings in order to draw a data-driven map of New York.
This guide is intended to walk you through the process of analyzing a healthcare dataset with Graphext. We will build a prediction model that analyzes a dataset of 5110 patients who either did or did not suffer a stroke. We will analyze the most influential factors when considering the patients that suffered a stroke.
This guide is intended to walk you through the process of analyzing customer reviews with Graphext. We will analyze a dataset of 42,656 reviews about 3 Disneyland branches.
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.
This guide is focused on the process of building a model to understand the reasons why employees left their jobs.
Graphext allows you to analyze the conversation and activity of different accounts and audiences in Twitter. We can use Tractor to download the data we want to analyze, but Graphext also allows data from any sources ( Brandwatch, Talkwalker, Meltwater, Press Clippings...)
RFM and Product Basket Analysis Requirements
