As we have already seen in former posts simple methods can be surprisingly successful in yielding good results (see e.g Learning Data Science: Predicting Income Brackets or Teach R to read handwritten Digits with just 4 Lines of Code).
If you want to learn how some simple mathematics, known as Naive Bayes, can help you find out the sentiment of texts (in this case movie reviews) read on!
Continue reading “Learning Data Science: Sentiment Analysis with Naive Bayes”
It has been an old dream to teach a computer to see, i.e. to hold something in front of a camera and let the computer tell you what it sees. For decades it has been exactly that: a dream – because we as human beings are able to see, we just don’t know how we do it, let alone be precise enough to put it into algorithmic form.
Enter machine learning!
Continue reading “Teach R to see by Borrowing a Brain”
Customer Relationship Management (CRM) is not only about acquiring new customers but especially about retaining existing ones. That is because acquisition is often much more expensive than retention. In this post, we learn how to analyze the reasons of customer churn (i.e. customers leaving the company). We do this with a very convenient point-and-click interface for doing data science on top of R, so read on!
Continue reading “Data Science on Rails: Analyzing Customer Churn”
In 1965 the University of Chicago rejected Kurt Vonnegut’s college thesis, which claimed that all stories shared common structures, or “shapes”, including “Man in a Hole”, “Boy gets Girl” and “Cinderella”. Many years later the then already legendary Vonnegut gave a hilarious lecture on this idea – before continuing to read on please watch it here (about 4 minutes):
Continue reading “Extracting basic Plots from Novels: Dracula is a Man in a Hole”
The two most disruptive political events of the last few years are undoubtedly the Brexit referendum to leave the European Union and the election of Donald Trump. Both are commonly associated with the political consulting firm Cambridge Analytica and a technique known as Microtargeting.
If you want to understand the data science behind the Cambridge Analytica/Facebook data scandal and Microtargeting (i.e. LASSO regression) by building a toy example in R read on!
Continue reading “Cambridge Analytica: Microtargeting or How to catch voters with the LASSO”
A few month ago I posted about market basket analysis (see Customers who bought…), in this post we will see another form of it, done with Logistic Regression, so read on…
Continue reading “Learning Data Science: The Supermarket knows you are pregnant before your Dad does”
A few months ago I published a quite popular post on Clustering the Bible… one well known clustering algorithm is k-means. If you want to learn how k-means works and how to apply it in a real-world example, read on…
Continue reading “Learning Data Science: Understanding and Using k-means Clustering”
It can be argued that the most important decisions in life are some variant of an exploitation-exploration problem. Shall I stick with my current job or look for a new one? Shall I stay with my partner or seek a new love? Shall I continue reading the book or watch the movie instead? In all of those cases, the question is always whether I should “exploit” the thing I have or whether I should “explore” new things. If you want to learn how to tackle this most basic trade-off read on…
Continue reading “Reinforcement Learning: Life is a Maze”
What is the best way for me to find out whether you are rich or poor, when the only thing I know is your address? Looking at your neighbourhood! That is the big idea behind the k-nearest neighbours (or KNN) algorithm, where k stands for the number of neighbours to look at. The idea couldn’t be any simpler yet the results are often very impressive indeed – so read on…
Continue reading “Teach R to read handwritten Digits with just 4 Lines of Code”
You may have misread the title as the old correlation does not imply causation mantra, but the opposite is also true! If you don’t believe me, read on…
Continue reading “Causation doesn’t imply Correlation either“