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Guiding you to build data science projects remarkably fast, collaboratively and without writing code.
More powerful than dashboards and more intuitive than notebooks.

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Inside Graphext.

The Graph
Compare
Trends
Details
Insights
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Connect Data.

Pull data directly from databases like PostgreSQL, MySQL, Google BigQuery and more. Integrate with Google Drive and Sheets.
Drag and drop files into your Graphext workspace to start working with datasets. We support a range of file types including CSV, JSON and SAV.
Use Tractor to create new datasets from information posted on popular platforms including Twitter and Google News.
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Build Projects.

Create and deploy powerful clustering and prediction models on your data using the guidance of our setup wizard.
Filter and sample your data directly from your Graphext workspace. Switch and control the variable types in your data.
Enrich your analysis with information from popular APIs or external sources with census, climate or socio-demographic data.
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Explore & Analyze.

Discover communities and their features using the visual representation of the Graph to explore your datasets.
Find temporal patterns in your data at a glance. Switch between chart types and detect anomalies and trends automatically.
Uncover the relevance of variables in explaining similarities and differences between segments of your data.
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Report Insights.

Capture your findings as you move through your analysis. Create reports to help tell the stories in your data.
Save and download charts, tables and insights as exportable image files.
Publish and present your Graphext project. Embed projects in any website to start sharing aspects of your analysis online.
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Resources

Reverse Engineering Infamous Marketing Strategies from Innocent Drinks

Why are the social media strategies of Innocent Drinks considered as the gold standard for marketing teams the world over? We collected every tweet (10,521) posted by the communication department to deconstruct Innocent's content, style, reach and engagement with a simple topic analysis.

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How Aquaservice Use Graphext To Improve Their Prediction Models

We spoke to the data science team at Aquaservice about how they used Graphext to build a clustering model to improve the way they forecast consumer demand. Their project grouped delivery routes using over 30 factors to calculate similarity and exposed patterns in the errors made by their prediction models. Models that are responsible for forecasting the number of water bottles that should be loaded into trucks for delivery across a huge number of routes across Spain.

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What People Have Felt and Thought About The 2021 UN Climate Change Conference (COP26)

We collected every tweet published about the 2021 UN Climate Change Conference (COP26) to study how people have engaged with events during the summit. Using topic analysis and emotion detection, our project dives into people's visceral reactions to agreements on deforestation, commitments between China and the USA and the appearance of Barack Obama.

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How to Perform Simple & Effective Customer Segmentation | A Walkthrough with Data from a Delicatessen

Customer segmentation involves splitting a customer base into distinct groups. These customer segments are defined by specific and shared characteristics, behaviours or preferences that help businesses to spot patterns and associate customers with one another. This article walks through the steps involved in a simple customer segmentation analysis. Using sales data from a delicatessen, we'll segment customers according to their buying preferences and behaviour. To achieve this, we'll use a powerful machine learning technique known as clustering.

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A digest of our blog data analysis, product updates and company news
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