Publications by Longhow Lam
The Eurovision 2016 song contest in an R Shiny app
Introduction In just a few weeks the Eurovision 2016 song contest will be held again. There are 43 participants, two semi-finals on the 10th and 12th of May and a final on the 14th of May. It’s going to be a long watch in front of the television…. Who is going to win? Well, you could ask experts, lookup the number of tweets on the different ...
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New chapters for 50 shades of grey….
Introduction Some time ago I had the honor to follow an interesting talk from Tijmen Blankevoort on neural networks and deeplearning. Convolutional and recurrent neural networks were topics that already caught my interest and this talk inspired me to dive into these topics deeper and do some more experiments with it. In the same session organize...
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Some insights in soccer transfers using Market Basket Analysis
Introduction Although more than 20 years old, Market Basket Analysis (MBA) (or association rules mining) can still be a very useful technique to gain insights in large transactional data sets. The classical example is transactional data in a supermarket. For each customer we know what the individual products (items) are that he has put in his bas...
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Danger, Caution H2O steam is very hot!!
H2O has recently released its steam AI engine, a fully open source engine that support the management and deployment of machine learning models. Both H2O on R and H2O steam are easy to set up and use. And both complement each other perfectly. A very simple example Use H2O on R to create some predictive models. Well, due to lack of inspiration I ...
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Don’t buy a brand new Porsche 911 or Audi Q7!!
Introduction Many people know that nasty feeling when buying a brand new car. The minute that you have left the dealer, your car has lost a substantial amount of value. Unfortunately this depreciation is inevitable, however, the amount depends heavily on the car make and model. A small analysis of data from (used) cars shows these differences. Ca...
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Don’t give up on single trees yet…. An interactive tree with Microsoft R
Introduction A few days ago Microsoft announced their new Microsoft R Server 9.0 version. Among a lot of new things, it includes some new and improved machine learning algorithms in their MicrosoftML package. Fast linear learner, with support for L1 and L2 regularization. Fast boosted decision tree. Fast random forest. Logistic regression, with ...
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Did you say SQL Server? Yes I did….
Introduction My last blog post in 2016 on SQL Server 2016….. Some years ago, I have heard predictions from ‘experts‘ that within a few years Hadoop / Spark systems would take over traditional RDBMS’s like SQL Server. I don’t think that has happened (yet). Moreover, what some people don’t realize is that at least half of the world stil...
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R formulas in Spark and un-nesting data in SparklyR: Nice and handy!
Intro In an earlier post I talked about Spark and sparklyR and did some experiments. At my work here at RTL Nederland we have a Spark cluster on Amazon EMR to do some serious heavy lifting on click and video-on-demand data. For an R user it makes perfectly sense to use Spark through the sparklyR interface. However, using Spark through the pySpark...
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Because its Friday… The IKEA Billy index
Introduction Because it is Friday, another ‘playful and frivolous‘ data exercise IKEA is more than a store, it is a very nice experience to go through. I can drop of my two kids at smàland, have some ‘quality time’ by walking around the store with my wife and eat some delicious Swedish meatballs. Back at home, the IKEA furniture are a g...
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Test driving Python integration in R, using the ‘reticulate’ package
Introduction Not so long ago RStudio released the R package ‘reticulate‘, it is an R interface to Python. Of course, it was already possible to execute python scripts from within R, but this integration takes it one step further. Imported Python modules, classes and functions can be called inside an R session as if it were just native R funct...
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