What did we learn from joining Kaggle’s Commonlit Readability prize? Well, next to having a lot of fun, it definitely helped us to explore the text analytics landscape even further. You can read our whole story in this article
For our latest Project Friday, we entered the CommonLit Readability competition on Kaggle. We learned a lot from working together on this cool NLP task. Stay tuned to see where this led us.
Starting to build machine learning models using Python can surely be overwhelming due to the endless possibilities that this open source tool offers. Therefore, we built a Python package that guides (junior) data analysts and scientists through all the steps involved in building machine learning models with easy to use functions.
Almost every day we go online we encounter recommender systems; if you are listening to your favorite song on Spotify, binge watching a TV show on Netflix or buying a new laptop on Amazon. Although we all know these recommendation engines exist, it is less known what algorithms lie behind such recommendations. To get a better understanding of the algorithms used in recommender systems we decided to build a recommender ourselves! With COVID-19 making us more housebound than ever, a topic for our recommendation engine was quickly found; we decided to build a book recommender using Python. Although there are ready-to-use packages to build recommender systems (such as Surprise) we decided to built or own recommendation system. We did this because our goal was to understand how a recommendation engine works rather than just have a book recommender. Moreover, we wanted to be able to control the specifications of the variables used in our engine and wanted to avoid the black box. In this notebook we explain step-by-step how we built our recommendation engine.
The world of Data Science is changing in such a rapid pace and into such broad directions that it is impossible to even be informed on every aspect of our interesting work field, let alone use all of these marvelous insights in practice. Therefore, at The Analytics Lab and Cmotions, we focus on the parts of Data Science […]
In the last couple of years, my Data Science attention went mostly towards text (NLP / NLU), but that does not prevent me from playing around with video. Inspired by Trump’s response to his Corona approach and Jim Carrey vs Allison Brie, see my first attempt at playing with DeepLearning for video and DeepFakes. While […]
Deze week is er in de haven van Rotterdam een flinke partij Mexicaans bier onderschept. Om verdere verspreiding van deze verslavende drank te voorkomen, heeft de regering voor de komende weken strenge beperkingen gesteld op alle goederen die vanuit risicogebieden geïmporteerd worden. Dit betekent dat ook de import van tortillachips, avocado’s en jalapenopepers de komende […]
Al jarenlang staan de kranten er vol van: Nederland dreigt een nachostaat te worden. Het gebruik van tortillachips in het uitgaansleven is eerder een regel dan uitzondering en er vaart geen vrachtschip uit Zuid-Amerika de haven binnen zonder verstopte pakketjes jalapeño pepers en crème fraîche in bananendozen. In schimmige Nederlandse kelders worden avocado’s versneden tot […]
After almost seven months, we finally came back with a brand new Meetup! On the 15th of May we came together at our headquarters in Amersfoort. Although there were probably many who stayed home that night to watch football, still thirty people were joining our Meetup. Good choice because we had a really interesting evening! Jeanine Schoonemann started […]