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The inspiration for this post is a joint venture by both me and my husband, and its genesis lies more than 15 years in our past. One of the recurring conversations we have in our relationship (all long-term relationships have these, right?!) is about song lyrics... Read more
This is the second article in our two-part series on using unsupervised and supervised machine learning techniques to analyze music data from Pandora and Spotify. Introduction As you may recall from the previous post I did, where I applied dimensionality reduction and clustering techniques to a set of songs... Read more
Last Saturday, in the UEFA Champions League final (think of it as Europe’s Super Bowl), Spanish giants Real Madrid defeated their Italian counterparts Juventus FC 4-1. It was a thrilling match, that saw both sides staking an equal claim to winning the match in the first half, with... Read more
Here at Tryolabs we love Python almost as much as we love machine learning problems. These kind of problems always involve working with large amounts of data which is key to understand before applying any machine learning technique. To understand the data, we need to manipulate... Read more
It’s been a while since my last post on some TB WHO data. A lot has happened since then, including the opportunity to attend the Open Data Science Conference (ODSC) East held in Boston, MA. Over a two day period I had the opportunity to listen to a... Read more
In this post we will describe how to evaluate a predictive model. Why bother creating complex predictive models if 5% of the customers will churn anyway? Because a predictive model will rank our clients based on the probability that they will abandon the company. It helps answer these... Read more
How we created an illustrated guide to help you find your way through the data landscape. Designing Data-Intensive Applications, the book I’ve been working on for four years, is finally finished, and should be available in your favorite bookstore in the next week or two. An... Read more
Choroplethr version 3.6.0 is now on CRAN. This version adds functionality for getting and mapping demographics of US Census Tracts. You can install it from the R console as follows: 1 2 3 install.packages("choroplethr") packageVersion("choroplethr") ‘3.6.0’ To use this functionality you will need an API... Read more
Shiny Project contributed by Amy Tzu-Yu Chen – Data Science Student in the NYC Data Science Academy Bootcamp Motivation In 2013, Mayor De Blasio campaigned on a promise of universal pre-kindergarten. The program makes access to free pre-kindergarten education available to all NYC families, regardless of child’s abilities and... Read more
Today’s guest post is by Julia Silge. Take a look at her work on (“Mapping US Religion Adherence by County in R“) where she demonstrated how to work with US religion adherence data in R. In this post she explores the relationship between that dataset and US... Read more