In a previous post, I showed you how to scrape playlist data from Columbus, OH alternative rock station CD102.5. Since it's the end of the year and best-of lists are all the fad, I thought I would share the most popular songs and artists of the year, according to this data. In addition to this, I am going to make an interactive graph using Shiny, where the user can select an artist and it will graph the most popular songs from that artist.
CD1025’s Playlist and Summerfest Last time, I showed you how to download CD1025’s playlist back to last year and did some exploratory analysis to find that there were some gaps in the data. Using this data, I would like to look at the artists that are playing in this week’s Summerfest. Summerfest is one of the biggest shows that CD1025 puts on every year and is hyped quite a bit on the station.
CD1025 is an “alternative” radio station here in Columbus. They are one of the few remaining radio stations that are independently owned and they take great pride in it. For data nerds like me, they also put a real time list of recently played songs on their website. The page has the most recent 50 songs played, but you can also click on “Older Tracks” to go back in time.
Note: I started this post way back when the NCAA men's basketball tournament was going on, but didn't finish it until now.
Since the NCAA Men's Basketball Tournament has moved to 64 teams, a 16 seed as never upset a 1 seed. You might be tempted to say that the probability of such an event must be 0 then. But we know better than that.
In this post, I am interested in looking at different ways of estimating how the odds of winning a game change as the difference between seeds increases.
The famous probabilist and statistician Persi Diaconis wrote an article not too long ago about the "Markov chain Monte Carlo (MCMC) Revolution." The paper describes how we are able to solve a diverse set of problems with MCMC. The first example he gives is a text decryption problem solved with a simple Metropolis Hastings sampler.
I was always stumped by those cryptograms in the newspaper and thought it would be pretty cool if I could crack them with statistics.
Restricted Boltzmann Machines (RBMs) are an unsupervised learning method (like principal components). An RBM is a probabilistic and undirected graphical model. They are becoming more popular in machine learning due to recent success in training them with contrastive divergence. They have been proven useful in collaborative filtering, being one of the most successful methods in the Netflix challenge (paper). Furthermore, they have been tantamount to training deep learning models, which appear to be the best current models for image and digit recognition.
With the election nearly upon us, I wanted to share an easy way I just found to download polling data and graph a few with ggplot2. dlinzer at github created a function to download poll data from the Huffington Post's Pollster API.
The default is to download national tracking polls from the presidential election. After sourcing the function, I load the required packages, download the data, and make the plot.
Finding the best subset of variables for a regression is a very common task in statistics and machine learning. There are statistical methods based on asymptotic normal theory that can help you decide whether to add or remove a variable at a time. The problem with this is that it is a greedy approach and you can easily get stuck in a local mode. Another approach is to look at all possible subsets of the variables and see which one maximizes an objective function (accuracy on a test set, for example).