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Machine Learning for Retail: Predict Your Customers' Next Purchase

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Data Science Festival Dataiku Event (Ballot ticket only)


Please register for a ballot ticket here:

Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event at Dataiku on Wednesday 26th April 2017, the ballot will be drawn on the 19th April 2017. Those randomly selected will then be e-mailed tickets for the event, with the joining details.


Big data is poised in the coming years to open up huge opportunities in the way retailers fundamentally operate and serve customers. The best retailer data science teams are finding efficiency improvements everywhere, from the supply chain to customer marketing, using data and transforming their business.

But apart from the buzzwords, how can data teams use predictive analytics to deliver real value? In this meet-up, you'll see how customer's next purchase can be predicted with analytics, running on HDInsight and Dataiku

As anyone who has attended our meet-ups before will testify, you can expect excellent talks, discussion, and complimentary pizza and beer for everyone!

- "From Data ingestion to data viz, how to start and complete an analytics project for retail on Azure HDInsight Application Platform" Nicolas Caudron, Strategy Architect at Microsoft & Elastacloud

-" Customer Next Purchase Prediction for Retail: a Global Use Case" Thomas Cabrol, Lead Data Scientist at Dataiku

Microsoft Abstract
How retailers can leverage the Azure HDInsight Application Platform ( to use applications that span a variety of use cases like data ingestion, data preparation, data processing, building analytical solutions and data visualization.
Concrete use cases but also hands-on demos!

Dataiku Abstract
The retail industry has been data centric for a while. With the rise of loyalty programs and digital touch points, retailers have been able to collect more and more data about their customers over time, opening up the ability to create better, more personalized marketing offers and promotions.

In this talk, leveraging open datasets and advanced models, you'll see how Machine Learning enhances a better, more pro-active and more efficient customer's behaviour prediction.
Using transactional, demographic, and product data, we will walk you through the entire data science process for predicting a customer’s next purchase (from data ingestion to model deployment).