Tag Archives: machine learning

The Barclays Data Science Hackathon: Building Retail Recommender Systems based on Customer Shopping Behaviour

In the depths of the last cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a one week hackathon where they collaboratively developed a recommendation system on top of Apache Spark. The contest consisted on using Bristol customer shopping behaviour data to make personalised recommendations in a sort of Kaggle-like competition where each team’s goal was to build an MVP and then repeatedly iterate on it using common interfaces defined by a specifically built framework. Continue reading

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Surfing and Coding in Lanzarote, the Barclays Data Science hackathon

This post has been published on the Cloudera blog and summurises the results and takeaways of a week-long hackathon happened in Lanzarote in December 2015. The goal was to prototype a recommender systems for retail customers of shops in Bristol in Bristol, UK. The article shows how the stack composed by Scala and Spark was great for quickly writing some prototyping code to run locally in a single laptop and at the same time scalable for larger dataset to process in the cluster. Continue reading

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Robust and declarative machine learning pipelines for predictive buying

Proof of concept of how to use Scala, Spark and the recent library Sparkz for building production quality machine learning pipelines for predicting buyers of financial products.

The pipelines are implemented through custom declarative APIs that gives us greater control, transparency and testability of the whole process.

The example followed the validation and evaluation principles as defined in The Data Science Manifesto available in beta at http://www.datasciencemanifesto.org Continue reading

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Lessons learnt from building data-driven production systems at Barclays

In the last years at Barclays we learnt and tried a lot of stuff that made the Advanced Analytics team very successful inside a large organization where, as such, being a productive data scientist is a tough challenge.

Our team works on a mix of descriptive, predictive and prescriptive projects that make use of machine learning and big data technologies, mainly on top of Apache Spark. Even though we deliver per-request insights coming from manual analysis, we primarily build automated and scalable systems to be periodically used either internally for a better decision-making or customer-facing in the form of analytics services (e.g. via the web portal).
In this post series I want to share some of the best practices, tools, methodologies and workflows that we experimented and the lessons learnt from them. Continue reading

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The balance of exploratory analysis and development

Exploratory analysis should precede and follow any task from the modelling, design and development to the benchmarking. Major problem is how do you share, track and monitor your findings? How do you make your analysis repeatable and scrutinizable from the outside? This is still an open problem. Continue reading

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Data Clustering? don’t worry about the algorithm. Introduction post of Data Clustering Tuning published on AgilOne blog on May 2013. We are constantly pushing to improve our underlying algorithms and make them as adaptive as possible. Taking a step back, … Continue reading

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