In our first post about performance measures we talked about Cohen’s kappa. This time I want to talk about two different measures: The Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE). The RMSE is defined as: It is one of the most common metrics in regression, both in Read more…
Finance is one of the industries with the best adoption rates of machine learning and AI methods. This makes perfect sense since companies in finance all share, more or less, these features: They handle large volumes of data. They have a large number of employees accustomed to quantitative methods. Even Read more…
Today’s post is provided by Kite Table of Contents Introduction: balanced and imbalanced datasets What is data oversampling? What is SMOTE? How does SMOTE work? SMOTE tutorial using imbalanced-learn Base model Imbalanced model SMOTE’d model Recap and conclusion Introduction: balanced and imbalanced datasets Close your eyes. Now imagine a perfect Read more…
We live in the era of personalisation. Through data science it becomes possible to suggest, discover and create products that are tailor-suited to each individual’s preferences. The first wave of personalisation through data science came in the form of recommender systems. Recommendation engines predict what you are going to like, Read more…
QA Testing Most people are familiar with the term Quality Assurance Testing in engineering. The definition of Q&A Testing given by techtarget is: “In developing products and services, quality assurance is any systematic process of checking to see whether a product or service being developed is meeting specified requirements. […] A quality assurance Read more…
Every year I am teaching social media analytics at the Cyprus International Institute of Management. The course includes topics such as natural language processing, and how to extract insights from social media. Last year I was lucky to have Valerios Paris Mitsakis in my classroom, who did a very impressive Read more…
Mining opinions on the web Sentiment analysis is one of the most successful and widespread applications in natural language processing. However, for all the hype it has generated since its inception, there are still many issues associated with it. In my work with Brandtix and other startups I had the opportunity Read more…
It is impossible to become a data scientist without programming. However, you don’t have to be a data scientist to use data science! The market around data science, machine learning and analytics has matured enough to the point where there are many products out there to run data science algorithms Read more…
Black boxes in machine learning I was reading Kaggle’s No Free Hunch blog the other day. It had an interview from Josef Slavicek who ranked 3rd place on the “Flavours of Physics” competition. Something that clearly stood out was that Josef is not a data scientist and he managed to Read more…
Data science rules the world It’s impossible these days to be in the world of tech and not hear about data, AI and machine learning. Everyone is talking about things like data science, using data to drive their decisions, improving their efficiencies and increasing their sales using insights generated from Read more…