Category Archives: R

Data Analytics modelling, why tune by hand?

When we’re carrying out analysis once we’ve got clean transformed data we have to create a model. There are many types of models that can be used depending on the type of analysis or prediction being made. ¬†For instance, predicting … Continue reading

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Consuming R through API endpoints with Domino Data Lab

This is a follow-up to the post about deploying R models using web services. Within 1 hour I was able to take an existing R function and publish to the Domino Data Lab web service AND write a simple Python … Continue reading

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Deploying R/Python scripts

When we clean and transform data and go on to produce a great predictive model the last thing we want is to not deploy it. The issue we have is deployment often means heavy engineering. Depending on the resources at … Continue reading

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Interactive time series with 3 lines of R

Time Series analysis is a fundamental aspect of performance management.   Before we can begin to model time-series there’s huge dividend in just visualising the data.  We can see if there is a seasonal pattern, or is the time-series additive or … Continue reading

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Random Forests with R

This post covers modelling and evaluation steps from the CRISP-DM methodology. When making decisions in business it’s prudent to use business facts. I’m sure I have no need to convince you of this. Sometimes the decision we’re trying to make involves … Continue reading

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