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Machine Learning with R – Barbara Fusinska

On one of the coldest nights of the year, 100 people ventured out to hear Barbara (Basia) Fusinska talk about Machine Learning with R at the London Business Analytics Group. If you missed it, Skills Matter, our hosts for the evening have recorded the event here. But if you want a quick summary in a few minutes, read on…

Barbara started by introducing machine learning (ML), gave a brief overview of R and then discussed three examples; classifying hand written digits, estimating values in a socio-economic dataset and clustering crimes in Chicago.

ML is statistics in steroids. ML uses data to find that pattern then uses that pattern (model) to predict results from similar data. Barbra uses the example of classifying film genres into either action or romance based on the number of kicks and kisses. Barbara described supervised and unsupervised. Unsupervised is the “wild, wild west” we can’t train the model and it is much more difficult to understand how effective these are.

Back to supervised learning, it’s important to choose good predicting factors – in the movie example perhaps the title, actors, script may have been better predictors that the number of kicks and kisses. Then you must choose the algorithm and then tune it and finally make it useful and visible and get it into production – it’s a hard job especially when data scientists and software developer seem to be different tribes. (In fact Richard Conway gave a talk about this very topic back in November – it is here.)

Because R takes both the good and the bad from the open source world it can introduce a little messiness. It was built by statisticians, not software engineers. So for example there are always several different ways of achieving the same thing. It’s a great language for exploratory data analysis and data wrangling. R can build a model and make beautiful visualisations. It’s easy to get started – just download R and the free version of RStudio.

Barbara explained a few data science algorithms both in a mathematical and intuitive sense; these are k nearest neighbours, Naïve Bayes and logistic regression.   We use certain metrics to tell us how well our models predict. Accuracy is the number of correct predictions divided by the total number of prediction. It’s not always the best measure – for example when testing for a rare disease, predicting that nobody has the disease is highly accurate but not very useful. Other measures such as precision, sensitivity and specificity may be more appropriate.
Our first machine learning task was to classify images of handwritten digits such as this one below.

The images had already converted into a CSV file. This had a row of 64 numbers to represent the image and the 65th column for the actual value (as classified by a person). Barbara showed just a few lines of R code to train a k-nearest neighbours model based on a dataset of about 4,000 examples then tested that model against a test dataset based on about 2000 examples. She showed a confusion matrix below. This compares the actual values in the test dataset against the values predicted by the model. As you can see the model is very accurate. The model has a few problem with 8, mistakenly predicting a 9 or a 3 in a few cases.

R has several datasets built in. One of these, the Prestige dataset has socio-economic data. R can quickly show the relationships between the variables with the pairs() function.

That helps us to decide the parameters to build a linear regression model to predict the prestige value given the other variables – education, income and the percentage of women in a profession and job type (blue collar, white collar or professional, indicated above by the colour of the dots). R can build a model in a single line of code – in fact building statistical models this is what R was built for. The R model can also tell us which of the variable are most useful in predicting prestige – and it’s important to look at these. There are some other statistical information to determine how well the data fits the model; R-squared, p-values, scale-location, residuals and QQ plots.

Barbara described the k-means algorithm and used it for clustering crimes in Chicago. R does a great job of clustering this data as then visualising it on the map below – and does it in very few lines of code.

Barbara expressed some scepticism about the usefulness of this – firstly it a moot point how many clusters to choose but more importantly if you can visualise the data then why use Machine Learning to cluster it? We could have easily split into cluster by drawing lines on the map by hand.  We should think hard about our data and our objectives as we crunch our data, build our models and visualise our plots and maps in R.

Barbara blogs at barbarafusinska.com, tweets at @BasiaFusinska. The R Scripts for the three examples are available at https://github.com/BasiaFusinska/RMachineLearning.

Using R With Small Data – Links

Here are the links and demos from my talk on ‘Using R With Small Data’ at the Infiniteconf in London on July 6th 2016.

This OneDrive folder contains the materials for the talk. The files are the slide deck (in PDF format) and the generated html from the R markdown document.

The European Banking Authority (EBA) 2016 Stress Test Results online map tool is here.

Data Insights ‘Visualising Financial Data’ Talk – Links

Here are the links and demos from my talk on ‘Visualising Financial Data Using Power BI and R’ at the Data Insights Summit.
The video recording on youtube is here.
The code for the demos are on my GitHub page .  They include
  • Text Mining of Corporate Responsibility Reports
  • Operational Risk And Controls Assessment (RCSA) Visualisations
  • Large Project Gantt Style Visualisations
  • Visualisations based on the Gapminder dataset
They include the Power BI Desktop files and the R Scripts.
The European Banking Authority (EBA) 2016 Stress Test Results are here.
The online map tool is here.  Hans Rosling’s famous YouTube video of ‘200 Years, 4 Minutes – The Joy of Stats’ is here.
The London Business Analytics Group meetup page is here.  This shows our upcoming events.  Recordings of a few of our previous talks are on the Skills Matter page here.

Links for ‘Banking On Machine Learning’ talk

I gave a talk to Microsoft Student Partners about incubating machine learning into a large organisation, in particular into a big bank.  The slide deck is here.  I mentioned several events and articles during the talk.  Here are the links.

Rohan Kopparapu (4th year UCL) and Microsoft Student Partner  wrote a blog post about the UCL Data Science Student Challenges.

The video recordings of the two recent talks  about building a data-driven culture in an organisation are  Transforming a Museum to be data-driven using R – Alice Daish and
From nothing to something, a local government journey- John Kelly.

The podcast of the BBC Radio 4 interview with Anthony Jenkins, ex-CEO of Barclays, and others about how fintech and ML are disrupting banking is available here.

I suggested some London-based meetups
https://www.meetup.com/London-Business-Analytics-Group/ (LBAG, my one)
https://www.meetup.com/London-PUG/
https://www.meetup.com/data-visual-London/
https://www.meetup.com/rladies-london/

The Economist article on the change of culture at Microsoft and its focus on cloud and AI is here.

 Dr Andy Pardoe runs a comprehensive list of AI and ML resources at homeAI.info as well as a personal site.  He has been named a Top 30 AI influencer recently.

The abstract for the talk is below.

Banking On Machine Learning

Many of the audience will have recently attended a hackathon at UCL. Over a weekend participants applied Azure Machine Learning to financial datasets supplied by Bloomberg and derived some insightful results – a fantastic achievement. This was for many their first taste of a hackathon and data science.  However, for many organisations, getting to the stage where they are doing hard data science as part of a viable project is a long way into the journey of introducing and successfully taking advantage of ML.

This talk looks at the experience of starting ML at a few organisations then focuses on the challenges faced by big diversified banks.  We look at why banks are rushing to embrace ML and consider both the  opportunities and threats that ML poses.  We’ll consider the steps to introduce and incubate ML within the bank; through awareness, education, crowdsourcing, mobilisation (including hackathons) and finally implementation of ML as the new normal.

We’ll conclude that the successful introduction of ML into an organisation is about communication, culture, change, marketing, business analysis… and finally the hard data science of the sort that we saw in the hackathon.

Power BI March Update – Links

I gave the March round-up talk at the London Power BI User Group last night, before John Kelly’s inspiring talk, without the benefit of slide deck or demos due to the AV issues.  So here is my slide deck and links of the events and resources I mentioned during my talk. And I have posted  8 minute video to demo the Power BI desktop February and March updates on youtube, and also on OneDrive

The details of the SQLBI ‘Mastering DAX Workshop’ training in London on March 22-23 are here.

The PASS Business Analytics Marathon on March 29th features two Power BI talks from London PUG organisers; Prathy Kamasani at 6pm and David Moss at 7pm.  Register here.

The PBI motion scatter diagram of the Gartner Group Magic Quadrant of the last several years is here.

Now that we can theme our colours, the color brewer site is a very useful resource for selecting good palettes of categorical, sequential or diverging colours that forms best practice.

The Power BI team video of the March desktop update is here.

The data storytelling course by Albert Cairo is here.

The Economist produce wonderful charts on a daily basis in their Graphic Detail section of their website.

My London Business Analytics meetup group is having a talk on Wednesday next week (15th March).  The subject is transforming a museum to be data driven with R. Sign-up here.

BA Marathon: Using R To Clean And Transform Small Data – Links

This page contains the links and references for my talk “Using R To Clean And Transform Small Data” at the PASS BA Marathon on 14th December 2016.

The video recording and slide deck  are  here.

The Microsoft Professional Program Certificate in Data Science has two R courses.  These are  the Introduction To R For Data Science and Programming With R For Data Science.  I also recommend the DataCamp online R courses.

The London Business Analytics Group (LBAG) meetup page where you can see upcoming events is here and the videos of some of the past events are here.  The London Power BI User Group (PUG) meetup page is here.  Mark Butler’s blog of recent PUG and LBAG events is here.

Visualising Risk

Risk Managers need to analyse and make decisions on data that changes at least daily. Each day trading and risk systems generate huge volume of data – millions, perhaps billions of rows. There is lot a variety within this deluge of data including trades, positions, sensitivities, prices and risk results such as value-at-risk (VaR). Reference data such as bank’s hierarchy, counterparty details, instrument details, countries, currencies are also required.

Dashboards and visualisations are essential to make sense of this. A good visualisation will provide a top-level view to bring out the big picture and overall trends, and highlight any outliers or exceptions that need attention. The best visualisations will then allow risk managers to drill down to the detail.

The next series of blogs will describe useful visualisations for showing certain aspects of risk and explain what features make them helpful. We will look at
• Tree maps for visualising VaR
• Mechanisms to drill down a firm’s hierarchy
• Bullet charts for comparing risk usage against limits
• Line charts to show P&L history and other time series

Links for ‘A Month Of Predictive Analytics’

I gave a short talk titled ‘A Month Of Predictive Analytics’ to the first meeting of the wonderful Meetup Mashup group on 18th June 2015. Here are the resources mentioned in the talk.

My slide deck is here.

Recent News about AI Machine Learning

Datasets

Tools

Books

  • Data Smart – John W Foreman
  • Practical Data Science With R – Nina Zumel and John Mount
  • Data Science For Business – Foster Provost and Tom Fawcett
  • Applied Predictive Analytics – Dean Abbot

Online Courses

My webinar for PASS Business Analytics Virtual Chapter (BAVC) comparing tools for exploratory data analysis is here together with previous webinars presented at PASS BAVC.