🎁 New Features

This month, we've been working on making Graphext easier and more intuitive to use. We've added features making it easier to spot relationships between variables using Compare charts and inspect data on your Graph quickly by controlling size mapping with greater precision. 

We've also added a new type of analysis making it possible to analyze the relationships between recurring items in your data. On top of this, we've extended the list of language support options we have so that you can analyze text written in Turkish and Arabic. 

01. Automatic Grouping in Compare Charts with Prediction Models

It just got much easier to start analyzing the variables highlighting relationships in your data when you build a prediction model in Graphext.

To make it faster for you to spot key variables, we've re-configured the way that the Compare panel generates charts explaining differences or similarities between values in your dataset.

Now, when you generate compare charts after building a model using the train and predict analysis type, Graphext will automatically display only important variable charts, hiding the variable charts that aren't as relevant to your analysis. You can change this by switching between the new categories of charts that we've created. 

Use the dropdown menus at the top of your Compare panel to start toggling between these variable collections. 

Compare chart collections available in train and predict projects:

All - All variables

Target - Modelled variable

Factors - Variables used to create the model

Other Variables - Variables not used as factors or target

Important - Target(s) and Factors

Internal Variables - Variables created by Graphext

None - Select a variable individually

How can I start ?

• Start by building a project using either the train and predict analysis type.
• Then, from the Compare panel of your project, choose some variables to compare.
• Graphext will automatically display the Important collection of variable charts.
• Toggle between collections using the dropdown menu at the top of your Compare panel.

02. Co-occurrence Flow in Models

We've added a new type of analysis called Co-occurrence. You can find it within the Models section of the project setup wizard. 

Co-occurrence analysis lets you find relationships between recurring items in your dataset. It helps you identify which items are most associated with other items.

Whilst it's already possible to conduct co-occurrence analysis with Text and Product Basket analysis types in Graphext, we built this flow to work with any kind of data. Use Models → Co-occurrence analysis to discover the associations between a range of entities which might be people, products or places. 

How can I start using it?

• Upload a dataset containing recurring items. 
• Start building your project using the setup wizard.
• Choose Models as your type of analysis.
• Choose Co-occurrence as your sub-analysis type. 
• Tell Graphext how you want to aggregate your data and which column contains your items.
• Execute the project and start discovering how items are related.

03. Node Size Ranges

Node sizes are a great way of exploring quantitative values in your Graph at a glance. You can now control the range of sizes that nodes are given using a sliding scale.

Select the node size icon from the icon collection at the top of your project's Graph to start customizing the range of node sizes presented in your project. You can control the top and bottom of the range using the slider. 

You can also control your node sizes from your project settings. If you want to save your node size configuration, you can do this using the node size slider inside of the project settings window. 

How can I start using it?

• Open your project and navigate to the Graph.
• Select the node size icon from the icon list at the top of your Graph.
• Move the range presented in the slider to change the size of your nodes.

04. Support for New Languages

Our team of engineers and data scientists have been busy improving Graphext's Natural Language Processing capacity so that it is possible to analyze content written in more languages using our text analysis flows. 

You can now analyze text written in Arabic and Turkish on top of the existing language support options we already have built-in. We've also made it much easier to extend the list of languages we support!

Read more about language support at Graphext here.

How can I start using it?

• Start with a dataset containing at least one text field and choose Text or Social Media as your analysis type using the project setup wizard.
• Inside of the Data Extraction tab, choose how you would like to set the language of your text.
• You can set languages manually or by inferring it directly from the text itself.
• That's it. Execute your project and delve into your analysis.

🐞 Bug Fixes & Improvements

• Fixed a bug causing the building of projects to fail after a user decided to delete data points and recreate the project without these points. 
• Solved issues surrounding an inability to save new manual segmentations.
• We've made it easier to build projects using datasets that match our built-in analysis types. When you build a project matching an analysis type, Graphext will make assumptions on the way you want to set up your project. You can still edit the configuration of projects matching your dataset using the new edit button that we've added - instead of moving backwards through the wizard. 

📖 Stories worth Sharing

The Moneyball Method: Using Data to Build a Football Dream Team (On a Budget)

Our team set out to build an exceptional football team for less than 100M Euros. Using data provided in the FIFA 2020/2021 dataset - the video game - we built a prediction model in order to find the key performance attributes for each position. Then, we used this to pick out a team of excellent but undervalued players.

Market Basket Analysis

Maria and Paul analyzed a dataset of products from a bakery in Edinburgh to discover the associations between menu items. In this video, they walk through the process of conducting a simple product association analysis that could be used for any e-commerce or retail business.