I am in the middle of creating a new German YouTube channel that is centered around data science and R! I put a lot of effort into it to serve the interests of the community.
If you want to be a part of the process, watch interesting videos with data-based analyses and look behind the scenes, please consider subscribing to the channel!
The number of subscriptions is also vital for the YouTube algorithm to recommend the videos to other viewers on the platform!
Continue reading “Please Subscribe to My New (German) Data Science YouTube Channel!”
In view of the current dramatic events in Afghanistan many wonder why the extensive international efforts to bring some stability to the country have failed so miserably.
In this post, we will present and analytically examine a fascinating theory that seems to be able to explain political (in-)stability almost mono-causally, so read on!
Continue reading “The “Youth Bulge” of Afghanistan: The Hidden Force behind Political Instability”
More and more decisions by banks on who gets a loan are being made by artificial intelligence. The terms being used are credit scoring and credit decisioning.
They base their decisions on models whether the customer will pay back the loan or will default, i.e. determine their creditworthiness. If you want to learn how to build such a model in R yourself (with the latest R ≥ 4.1.0 syntax as a bonus), read on!
Continue reading “Will I get my Money back? Credit Scoring with OneR”
In 2018 the renowned scientific journal science broke a story that researchers had re-engineered the commercial criminal risk assessment software COMPAS with a simple logistic regression (Science: The accuracy, fairness, and limits of predicting recidivism).
According to this article, COMPAS uses 137 features, the authors just used two. In this post, I will up the ante by showing you how to achieve similar results using just one simple rule based on only one feature which is found automatically in no-time by the
OneR package, so read on!
Continue reading “Recidivism: Identifying the Most Important Predictors for Re-offending with OneR”
During our little break, Valentine’s Day was celebrated. Yet for many, it was a depressing day because they are single and are looking for love.
Speed dating is a popular format (in times of Covid-19 also in virtual form) to meet many different potential soul mates in a short period of time. If you want to learn which factors determine “getting to the next round”, read on!
Continue reading “Cupid’s Arrow: How to Boost your Chances at Dating!”
We already had a lot of examples that make use of the
OneR package (install free from CRAN), which can be found in the respective Category: OneR.
Here we will give you some concrete examples from the area of research on Type 2 Diabetes Mellitus (DM) to show that the package is especially well suited in the field of medical research, so read on!
Continue reading “OneR in Medical Research: Finding Leading Symptoms, Main Predictors and Cut-Off Points”
In one of my most popular posts So, what is AI really? I showed that Artificial Intelligence (AI) basically boils down to autonomously learned rules, i.e. conditional statements or simply, conditionals.
In this post, I create the simplest possible classifier, called ZeroR, to show that even this classifier can achieve surprisingly high values for accuracy (i.e. the ratio of correctly predicted instances)… and why this is not necessarily a good thing, so read on!
Continue reading “ZeroR: The Simplest Possible Classifier, or Why High Accuracy can be Misleading”
Valentine’s Day is around the corner and love is in the air… but, shock horror, nearly every second marriage ends in a divorce! Unfortunately, I can tell you first hand that this is an experience you’d rather not have. In this post, we see how data science, in the form of the
OneR package and an interesting new data set, might potentially help you to avoid that tragedy… so read on!
Continue reading “The One Question you should ask your Partner before Marrying!”
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”
There are a million reasons to learn R (see e.g. Why R for Data Science – and not Python?), but where to start? I present to you the ultimate introduction to bring you up to speed! So read on…
Continue reading “Learning R: The Ultimate Introduction (incl. Machine Learning!)”